Liver biopsies for diagnosing cirrhosis, the last stage of chronic liver disease was analysed using imaging and it could able to determine the severity level of the disease. Liver lesions can be segmented for evaluation of tumor burden, therapeutic strategy, prognosis, and follow-up on the efficacy of therapy. Automatic technologies for malignancy identification and segmentation are preferable because manual segmentation is a laborious process that is prone to error. We thus propose an M-Net approach to cirrhosis of the liver segmentation. The liver datasets are collected initially as raw data, which, due to the existence of noise, must be normalized before the investigation can begin. With the help of the Bilateral Filter and the Wavelet Transform, we can get rid of the noise and improve the de-noised image. The Gabor Filter is used for feature extraction. Hybrid Genetic Algorithm (HGA) is used to pick the best feature subsets for classification. Ensemble Deep Convolutional Neural Network (EDCNN) method is applied to the classification process. MATLAB, a simulation program, is used to conduct the entire inquiry. In terms of accuracy, our system has outperformed the most sophisticated automatic techniques.

Binarized spiking neural networks optimized with a color harmony algorithm for liver cancer classification (BSNN-CHA-LCC) are proposed to classify liver cancer as normal and abnormal. Initially, fusion of an MRI dataset and CT-scan datasets of a liver cancer dataset were taken, and the input images were given to CWF-based preprocessing for removing noise and increasing the quality of input computed tomography (CT) and magnetic resonance imaging (MRI). The preprocessed images of CT and MRI are given to improve the non-sub sampled Shearlet transform (INSST) method-based feature extraction for extracting features. The extracted features were given BSNN to classify liver cancer as normal and abnormal. The proposed method was implemented, and the efficiency of the proposed BSNN-CHA-LCC method was evaluated under performance metrics, such as precision, sensitivity, F-scores, specificity, accuracy, error rate, and computational time. The proposed technique achieved23.03%, 11.56%, and 21.22% higher accuracy and 36.12%, 15.23%, and 27.11% lower error rates than the existing models, such as hybrid-feature analysis depending on machine-learning for liver cancer categorization utilizing fused images (MLP-LCC), Deep learning-based classification of liver cancer histopathology images utilizing only global labels (mask-RCNN-LCC), and deep learning based liver cancer identification utilizing watershed transform and Gaussian mixture method (DNN-GMM-LCC), respectively.

Abstract Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures. Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs. Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM. Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.