Accurate detection of white matter lesions in 3D Magnetic Resonance Images (MRIs) of patients with Multiple Sclerosis is essential for diagnosis and treatment evaluation of MS. It is strenuous for the optimal treatment of the disease to detect early MS and estimate its progression. In this study, we propose efficient Multiple Sclerosis detection techniques to improve the performance of a supervised machine learning algorithm and classify the progression of the disease. Detection of MS lesions become more intricate due to the presence of unbalanced data with a very small number of lesions pixel. Our pipeline is evaluated on MS patients data from the Laboratory of Imaging Technologies. Fluid-attenuated inversion recovery (FLAIR) series are incorporated to introduce a faster system alongside maintaining readability and accuracy. Our approach is based on convolutional neural networks (CNN). We have trained the model using transfer learning and used softmax as an activation function to classify the progression of the disease. Our results significantly show the effectiveness of the usage of MRI of MS lesions. Experiments on 30 patients and 100 healthy brain MRIs can accurately predict disease progression. Manual detection of lesions by clinical experts is complicated and time-consuming as a large amount of MRI data is required to analyze. We analyze the accuracy of the proposed model on the dataset. Our approach exhibits a significant accuracy rate of up to 98.24%.

COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve-Receiver Characteristic Operator (AUC-ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to 4510 CMC, 2022, vol.72, no.3 identify the infected regions and cancer of the brain through various imaging modalities.

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