Kidney Tumour Segmentation and Classification Using Deep Learning

Diagnosis and subtype classification of kidney cancer, one of the most lethal illnesses, are critical to improving the prognosis of individual patients. As a result, there is an immediate need to create automated technologies that can correctly classify kidney cancer into its various subtypes. Scientists in the biomedical sector have discovered that miRNA dysregulation can result in cancer. We propose a machine learning strategy for subtyping kidney cancer from miRNA genome data in this study. Using a combination of computational and experimental methods, we identified 35 miRNAs that help diagnose certain subtypes of kidney cancer. To categorise a given miRNA sample into kidney cancer subtypes, the proposed method uses Neighborhood Component Analysis (NCA) to extract discriminative features from miRNAs. Only a few kidney subtypes have been examined for classification in the literature. The miRNA quantitative read counts data was obtained from the Cancer Genome Atlas data source and used in the experimental study (TCGA). Three-score and five of the most discriminatory microRNAs (miRNAs) were chosen using the NCA method. The ML system achieved an average accuracy of 95% when using this selection of miRNAs to classify miRNAs involved in kidney cancer into distinct subtypes. In our research, we compared the performance of the K-Nearest Neighbor (KNN) method, which is currently in use, with that of our suggested system, which makes use of Long Short-Term Memory (LSTM)