Breast cancer is a worldwide concern, emphasizing the importance of early diagnosis for effective treatment. Convolutional networks (CNN) have long dominated medical vision tasks. Recently, vision transformers (VIT) have sparked interest in computer-aided diagnosis (CAD). Nonetheless, when ViTs are trained from scratch, they exhibit poorer performance than (CNNs), particularly in low-data scenarios. This discrepancy is even more pronounced in tasks involving medical image datasets, where data scarcity is a significant challenge. To this end, we proposes a Vision Transformer (ViT) model that has been pre-trained on extensive datasets, including (ImageNet) and is applied directly to sequences of mammography breast image patches for the classification of benign and cancerous tissue. The model was thoroughly assessed using the DDSM dataset containing 5970 benign and 7158 malignant Masses, yielding remarkable results, with an accuracy of 99.96
Melanoma is a pathology that poses a risk to the health of the global population, leading to various complications from skin lesions that can result in patient death if not detected early. Medical imaging diagnosis is an increasingly used procedure in current literature, making the clinical investigation process more effective for high-precision imaging diagnosis. Computational methods have been employed to assist medical diagnosis through Computer-Aided Diagnosis (CAD) systems. This study introduces a new approach called SDA-Detection Melanoma for fully automatic detection and segmentation of melanomas in dermoscopy examination images. In this work, different deep-learning networks were used for melanoma detection, combined with the use of fine-tuning and computational methods based on Parzen windowing, clustering, and region growth for melanoma region segmentation. The results were quite satisfactory, achieving a high accuracy rate of 96.39
Tourism has been an essence for the economic growth. The tourism has been growing steadily and application of technology in tourism has been ever-increasing. The tourism organizations have been putting the technology to forecast and meet the demand. Machine learning has been very helpful for such organizations in understanding the tourism statistics. In this paper, a bibliometric analysis was carried out using Dimensions as the database and VOSviewer as the analytical tool. A total of 271 documents were analyzed. Nearly 18
Cholangiocarcinoma (CC), commonly known as choledochal cancer, is the second most frequent type of liver cancer. It primarily affects the elderly population; however younger people are also being diagnosed with it. Hyperspectral imaging (HS) is a spectroscopy-based technique that consists of many images in a band of neighboring spectra, and the reflection of spectra of all pixels is reconstructed to obtain three-dimensional hypercube data. The goal of this study is to provide a solution for semantically segmenting microscopic HS images of choledochal tissues acquired from 174 patients. Principal Component Analysis was used to preprocess the HS images, followed by min max normalization. The proposed system is based on the UNet architecture. The proposed system achieved an accuracy of 68.09
Inpainting is the process of filling in missing or corrupted parts of an image using the information that is present in the rest of the picture. One application of inpainting is the recovery of incomplete depth maps. Depth maps represent the distance from a sensor to each point in the scene. They are becoming increasingly important in applications such as 3D object reconstruction, depth map completion for control of autonomous vehicles, and others. This paper presents three models for inpainting incomplete depth maps and an anisotropic metric. Our inpainting model is the solution of a second-order degenerate partial differential equation. The three used models are variations of the infinity Laplacian: the biased infinity Laplacian, the balanced biased infinity Laplacian, and the double-balanced infinity Laplacian. The contribution of this paper is threefold. First, we propose two new models that are variations of the infinity Laplacian. Second, we evaluate these models on the public data set KITTI Depth Completion Suite. Results show that the considered biased infinity Laplacian and the anisotropic metric perform better than the other proposed models and contemporary models.
