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Renal cysts are categorized as simple cysts and complex cysts. Simple cysts are harmless and complicated cysts are cancerous and leading to a dangerous situation. The study aims to implement a deep learning-based segmentation that uses the Renal images to segment the cyst, detecting the size of the cyst and assessing the state of cyst from the infected renal image. The automated method for segmenting renal cysts from MRI abdominal images is based on a U-net algorithm. The deep learning-based segmentation like U-net algorithm segmented the renal cyst. The characteristics of the segmented cyst were analyzed using the Statistical features extracted using GLCM algorithm. The machine learning classification is performed using the extracted GLCM features. Three machine learning classifiers such as Naïve Bayes, Hoeffding Tree and SVM are used in the proposed study. Naive Bayes and Hoeffding Tree achieved the highest accuracy of 98%. The SVM classifier achieved 96% of accuracy. This study proposed a new system to diagnose the renal cyst from MRI abdomen images. Our study focused on cyst segmentation, size detection, feature extraction and classification. The three-classification method suits best for classifying the renal cyst. Naïve Bayes and Hoeffding Tree classifier achieved the highest accuracy. The diameter of cyst size is measured using the blobs analysis method to predict the renal cyst at an earlier stage. Hence, the deep learning-based segmentation performed well in segmenting the renal cyst and the three classifiers achieved the highest accuracy, above 95%.

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.