Analytical review of deep neural techniques for breast tumor detection.

A tumor is an abnormal mass of tissue that develops when cell growth and division are accelerated. Cancerous tumors can develop, making them extremely harmful and needing early detection for safe excision The most difficult and crucial task in many medical image applications is tumor detection/segmentation because it typically requires a large amount of data and information. Deep learning techniques including CNN (convolutional neural networks), RNN (recurrent neural networks), and DBN are employed in this paper (Deep belief network). The BRATS 2019 dataset is used in this study, and machine learning methods such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forests are compared. Performance metrics such as Dice Score Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity are used to determine which technique performs the best overall based on these metrics. The findings of the segmented images yielded dice scores for the CNN technique of 0. S9, PPV of 0. S7, and Sensitivity of 0.90.