Background Preoperative diagnosis of multifocal and multicentric breast cancer (MMBC) is crucial for surgical planning either for mastectomy or performing conservative management. Contrast-enhanced spectral mammography (CESM) is more applicable compared to MRI, yet it shows lower sensitivity in the evaluation of disease extension. Aim of the work To compare the diagnostic accuracy of CESM in the detection of additional suspicious lesions in patients with breast cancer and the diagnosis of the MMBC compared to contrast-enhanced magnetic resonance imaging (CE-MRI). Results This retrospective study was performed during the period between January 2020 and January 2024 including 60 patients diagnosed as breast cancer with suspected multifocality or multicentricity, and they all underwent both CESM and breast contrast-enhanced MRI for preoperative staging. CESM sensitivity, specificity and diagnostic accuracy for the diagnosis of additional lesions were estimated and compared to CE-MRI with significance which was considered (p value < 0.05). The postoperative pathological results were considered as the gold standard test. Our study showed comparable sensitivity of CESM (97%), and slightly higher diagnostic accuracy (95%) compared to CE-MRI (sensitivity = 99% and diagnostic accuracy = 94%) with no significant differences and with significantly higher specificity (CESE = 67%& CE- MRI = 33%) making CESM another promising method of MMBC breast diagnosis providing similar dedicated morphological and functional description about the lesion. Conclusions CESM is a valuable imaging modality for evaluation and diagnosis of MMBC with comparable sensitivity and accuracy and higher specificity compared to MRI so it can be considered as a promising alternative technique to CE-MRI.

Foreign-body ingestion is most common among children and the elderly. It is considered a medical emergency and requires rapid and accurate imaging assessment. This study aimed to demonstrate the various CT imaging features and complications associated with the ingestion of sharp foreign bodies. Out of 123 patients with a history of foreign-body ingestion, this retrospective study included 80 patients with positive CT evidence of retained foreign bodies. These patients were evaluated based on gender, age, type of foreign body, location, and associated complications. The cohort consisted of 35 males and 45 females, with ages ranging from 1 to 77 years (mean age: 28.5 ± 5.4 years). The most common foreign bodies were fish bones (n = 20, 25

Alzheimer’s Disease (AD) is characterized by the gradual degeneration and decline of brain cells, leading to irreversible neurological changes. This study investigates advanced image enhancement techniques for improving AD diagnosis using brain MRI. The methods used include CLAHE (Contrast Limited Adaptive Histogram Equalization) to enhance local image contrast and ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks) to improve image resolution. These preprocessing methods improve MRI images and classification accuracy. An ensemble model of MobileNetV2 and DenseNet121, two efficient deep-learning models with feature extraction capabilities, were used as classifiers. This approach addresses challenges in AD diagnosis by leveraging deep learning for more accurate classification of brain tissue images. The model achieved an accuracy of 80.31% for MobileNetV2 and 89.22% for DenseNet121 when no enhancements were utilized. The model achieved accuracies of 92.34% and 89.38% for MobileNetV2 and DenseNet121, respectively, when enhancement methods were utilized, indicating strong dependability with the Kaggle dataset of MRI images. These findings underscore the efficacy of advanced image processing and deep learning in the early and accurate detection of Alzheimer’s disease.

Background Full-field digital mammography (FFDM) is the primary screening method for breast cancer, yet the number of cancers that can be missed with mammography is considerable, notably in female with dense breast. In this study, we compared the diagnostic yield and the clinical significance of FFDM for breast cancer detection in female with dense breasts versus its performance when complemented by automated breast ultrasound (ABUS). Results This retrospective study was performed during the period between January 2022 and December 2022 including 500 females with dense breast (ACR C&D), who underwent screening using FFDM and ABUS. The images were retrospectively interpreted, and statistical assessments were done comparing the FFDM results alone and after complemented with ABUS. Significance was considered at a p value less than 0.05. The use of FFDM with supplemental ABUS has reduced the numbers of recall and showed improved breast cancer detection with increased positive predictive value (from 74.5 to 83.5%). In comparison, using FFDM alone and associated with ABUS, there was moderate agreement with a kappa test of 0.51; p < 0.001. Conclusion ABUS can be a useful and powerful diagnostic imaging tool when adjunct to FFDM for screening of dense breast. In this study, ABUS showed less false-negative results and improved the sensitivity of cancer detection.

Determining indicators or retinal wounds is essential for accurate diagnosis and retinal disorders grading. To view the retinal microarchitecture and easily screen for anomalies, optical coherence tomography (OCT) images are utilized. Numerous studies have already tried to use OCT to overcome that issue. Throughout this study, we describe an OCT image-based transfer learning (TL) approach for the identification of four retinal diseases. This study compares four distinct models with one another. A MobileNetV2 model’s detection accuracy on the test set is 100%; an InceptionNetV3 model’s is 99.9%; an EfficientNet model’s is 99.38%; and a DenseNet model’s is 99.79%. The InceptionNetV3 model approaches the highest accuracy, while MobileNetV2 model achieves the maximum accuracy. The suggested method may influence the development of a tool for automatically identifying retinal disorders. The promising suggested architecture’s qualitative assessments and quantitative outcomes through creating a confusion matrix demonstrate how the suggested methodology can be utilized in healthcare settings as a diagnostic tool to assist medical professionals in making more accurate diagnoses.

Sometimes when diabetic retinopathy (DR) is found and treated quickly, vision loss can indeed be spared. This study deploys a deep learning (DL) model that can discover all 5 stages of DR more accurately than other methods. The proposed methodology shows two cases scenarios: case 1 with image enhancement using CLAHE and ESRGAN, and case 2 without image enhancement. Augmentation techniques are then employed to produce a balanced dataset with the identical criteria for both scenarios. The generated model using DenseNet-121 on the APTOS dataset outperformed other approaches for locating the 5 stages of DR, with an accuracy of 98.7 percent for case 1 and 81.2 percent for case 2. Using CLAHE and ESRGAN was shown to improve a model's performance and ability to learn.

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model's performance and learning ability.

One of the primary causes of blindness in the diabetic population is diabetic retinopathy (DR). Many people could have their sight saved if only DR were detected and treated in time. Numerous Deep Learning (DL)-based methods have been presented to improve human analysis. Using a DL model with three scenarios, this research classified DR and its severity stages from fundus images using the “APTOS 2019 Blindness Detection” dataset. Following the adoption of the DL model, augmentation methods were implemented to generate a balanced dataset with consistent input parameters across all test scenarios. As a last step in the categorization process, the DenseNet-121 model was employed. Several methods, including Enhanced Super-resolution Generative Adversarial Networks (ESRGAN), Histogram Equalization (HIST), and Contrast Limited Adaptive HIST (CLAHE), have been used to enhance image quality in a variety of contexts. The suggested model detected the DR across all five APTOS 2019 grading process phases with the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100%. Further evaluation criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS 2019. Furthermore, comparing CLAHE + ESRGAN against both state-of-the-art technology and other recommended methods, it was found that its use was more effective in DR classification.

Prolonged hyperglycemia can cause diabetic retinopathy (DR), which is a major contributor to blindness. Numerous incidences of DR may be avoided if it were identified and addressed promptly. Throughout recent years, many deep learning (DL)-based algorithms have been proposed to facilitate psychometric testing. Utilizing DL model that encompassed four scenarios, DR and its stages were identified in this study using retinal scans from the "Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 Blindness Detection" dataset. Adopting a DL model then led to the use of augmentation strategies that produced a comprehensive dataset with consistent hyper parameters across all test cases. As a further step in the classification process, we used a Convolutional Neural Network model. Different enhancement methods have been used to raise visual quality. The proposed approach detected the DR with a highest experimental result of 97.83%, a top-2 accuracy of 99.31%, and a top-3 accuracy of 99.88% across all the 5 severity stages of the APTOS 2019 evaluation employing CLAHE and ESRGAN techniques for image enhancement. In addition, we employed APTOS 2019 to develop a set of evaluation metrics (precision, recall, and F1-score) to use in analyzing the efficacy of the suggested model. The proposed approach was also proven to be more efficient at DR location than both state-of-the-art technology and conventional DL.

When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.

Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR. Methods The suggested methodology presents two examples, one with and one without picture augmentation. A balanced dataset meeting the same criteria in both cases is then generated using augmentative methods. The DenseNet-121-rendered model on the Asia Pacific Tele-Ophthalmology Society (APTOS) and dataset for diabetic retinopathy (DDR) datasets performed exceptionally well when compared to other methods for identifying the five stages of DR. Results Our propose model achieved the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100% for the APTOS dataset, and the highest test accuracy of 79.67%, top-2 accuracy of 92.%76, and top-3 accuracy of 98.94% for the DDR dataset. Additional criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS and DDR. Conclusions It was discovered that feeding a model with higher-quality photographs increased its efficiency and ability for learning, as opposed to both state-of-the-art technology and the other, non-enhanced model.

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

Objectives To compare three-dimensional (3D) turbo-spin-echo (TSE) isotropic sequences with two-dimensional (2D) sequences in the detection of meniscal tears and compare it with arthroscopic findings which was the gold standard method. Background MRI is the most common, noninvasive, and accurate imaging modality for knee injuries. The purpose of the study was to compare 3D TSE isotropic sequences and conventional 2D TSE at 3T MRI in the detection of meniscal tears. Patients and methods This study was a retrospective study on 95 patients [42 (44.2%) females and 53 (55.8%) males] with suspected meniscal injury who had undergone knee 3 T MRI sequences, including a series of 2D conventional sequences with additional 3D isotropic TSE sequence in the period from April 2018 to March 2019. Results The sensitivity and specificity of 3D TSE is higher (100 and 100%) compared with 2D TSE sequence (67 and 96%) with statistical significance (P<0.003) in radial tears. For other types of meniscal tears, both 3D TSE and 2D TSE sequences had similar diagnostic accuracy with nonstatistical significance between the sensitivity and specificity of both techniques. Conclusion We concluded that 3D TSE is a useful and reliable technique that has a diagnostic performance like the routine 2D TSE MR protocol for detecting meniscal tears at 3 T with superior detection of radial tears.

The coronavirus disease (COVID-19) is rapidly spreading around the world. Early diagnosis and isolation of COVID-19 patients has proven crucial in slowing the disease's spread. One of the best options for detecting COVID-19 reliably and easily is to use deep learning (DL) strategies. Two different DL approaches based on a pertained neural network model (ResNet-50) for COVID-19 detection using chest X-ray (CXR) images are proposed in this study. Augmenting, enhancing, normalizing, and resizing CXR images to a fixed size are all part of the preprocessing stage. This research proposes a DL method for classifying CXR images based on an ensemble employing multiple runs of a modified version of the Resnet-50. The proposed system is evaluated against two publicly available benchmark datasets that are frequently used by several researchers: COVID-19 Image Data Collection (IDC) and CXR Images (Pneumonia). The proposed system validates its dominance over existing methods such as VGG or Densnet, with values exceeding 99.63% in many metrics, such as accuracy, precision, recall, F1-score, and Area under the curve (AUC), based on the performance results obtained.

Background Coronavirus disease-2019 (COVID-19) disease was primarily described as a pandemic of respiratory illness, however, with the disease progression, variable cases with extrapulmonary manifestations have been reported all over the world. Severe acute respiratory-syndrome coronavirus- 2 infection can affect different body systems with the neurologic, abdominal, thromboembolic, cardiac, mediastinal, and hematological manifestations that had been reported in many literatures. The understanding of this multisystemic involvement is being better understood as the pandemic progresses. Objective The aim was to study the different neuroimaging findings in patients with severe acute respiratory- syndrome coronavirus-2 infection and their clinical and epidemiological characteristics. Patients and methods Our study was a retrospective study in the period between April 1 and August 30, 2020. It included 98 patients who were proved to be COVID-19 and showed neurological manifestations with neuroimaging abnormalities on computed tomography and/or MRI studies. Results This study included 98 COVID-19 patients that have neurological manifestations and acute neuroimaging abnormalities. There were 78 (88.7%) males and 20 (20.3%) females with mean age 58 ± 10.18 years old, ranging from 2 to 81 years. The most common neurologic manifestations were alteration of consciousness 71.4% (70 cases), confusion 31.6% (31 cases), and agitation 18.4% (18 cases). The most frequent MRI findings were acute infarcts 38 cases (38.7%) and cortical fluid- attenuation inversion-recovery signal abnormality 33 cases (33.7%). Extensive and isolated white matter microhemorrhages are seen in three patients (3.1%). Hemorrhagic brain lesions were associated with more severe clinical presentation, especially in ICU patients. Conclusion Many neurological complications of COVID-19 infection were encountered with a wide spectrum of neuroimaging findings. Different imaging modalities (computed tomography/MRI) have a great role in the assessment of these neurological complications to avoid any untreated causes of morbidity/mortality.

The Internet of Things (IoT) refers to a system of interconnected, internet-connected devices and sensors that allows the collection and dissemination of data. The data provided by these sensors may include outliers or exhibit anomalous behavior as a result of attack activities or device failure, for example. However, the majority of existing outlier detection algorithms rely on labeled data, which is frequently hard to obtain in the IoT domain. More crucially, the IoT’s data volume is continually increasing, necessitating the requirement for predicting and identifying the classes of future data. In this study, we propose an unsupervised technique based on a deep Variational Auto-Encoder (VAE) to detect outliers in IoT data by leveraging the characteristic of the reconstruction ability and the low-dimensional representation of the input data’s latent variables of the VAE. First, the input data are standardized. Then, we employ the VAE to find a reconstructed output representation from the low-dimensional representation of the latent variables of the input data. Finally, the reconstruction error between the original observation and the reconstructed one is used as an outlier score. Our model was trained only using normal data with no labels in an unsupervised manner and evaluated using Statlog (Landsat Satellite) dataset. The unsupervised model achieved promising and comparable results with the state-of-the-art outlier detection schemes with a precision of ≈90% and an F1 score of 79%.

Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets.

One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image's quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study's results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.

In this paper, we present an experimental approach that allows a humanoid robot to effectively plan and execute whole body motions, like climbing obstacles and straight stairs up or down, besides jumping over obstacles using only on-board sensing.Reliable and accurate motion sequence for humanoid employed in complex indoor environments is a necessity for high-level robotics tasks.Using the robot's own kinematics will construct complex dynamic motions.A series of actions to prevent the object from being performed on the basis of the identified object from the database of the robot, obtained using the robot's own monocular camera.As shown in real world experiments beside simulation using NAO H25 humanoid, the robot can effectively perform whole body movements in cluttered, multilevel environments containing items of various shapes and dimensions.

Background: Coronavirus related respiratory illness usually manifests clinically as pneumonia with predominant imaging findings of an atypical or organizing pneumonia. Plain radiography is very helpful for COVID-19 disease assessment and follow-up. It gives an accurate insight into the disease course. We aimed to determine the COVID-19 disease course and severity using chest X-ray (CXR) scoring system and correlate these with patients' age, sex, and outcome. Results: In our study, there were 350 patients proven with positive COVID-19 disease; 220 patients (62.9%) had abnormal baseline CXR and 130 patients (37.1%) had normal baseline CXR. During follow-up chest X-ray studies, 48 patients (13.7%) of the normal baseline CXR showed CXR abnormalities. In abnormal chest X-ray, consolidation opacities were the most common finding seen in 218 patients (81.3%), followed by reticular interstitial thickening seen in 107 patients (39.9%) and GGO seen in 87 patients (32.5%). Pulmonary nodules were found 25 patients (9.3%) and pleural effusion was seen in 20 patients (7.5%). Most of the patients showed bilateral lung affection (181 patients, 67.5%) with peripheral distribution (156 patients, 582%) and lower zone affection (196 patients, 73.1%). The total severity score was estimated in the baseline and follow-up CXR and it was ranged from 0 to 8. The outcome of COVID-19 disease was significantly related to the age, sex, and TSS of the patients. Male patients showed significantly higher mortality rate as compared to the female patients (P value 0.025). Also, the mortality rate was higher in patients older than 40 years especially with higher TSS. Conclusion: Radiographic findings are very good predictors for assessing the course of COVID-19 disease and it could be used as long-term consequences monitoring.