Analyzing Genomes using Deep Learning and Image Processing Techniques

This chapter is an attempt to address the requirement of better diagnosis tools in oncology and, specifically, in breast cancer cases, where images of histopathology must be classified accurately and early for proper treatment. The research aims to consider the performance of various deep learning models, such as VGG16, VGG19, Inception V3, and ResNet50, in classifying the stages of breast cancer by using images from sources such as Kaggle and hospitals. These images represented all stages of cancer, the latter being carefully labelled with professional medical support for high-quality training data. The techniques applied pre-trained models in a pre-processing step and then trained each of these on a subset of the data; performance metrics thus included accuracy, data loss, precision, recall, and F1 score, assessed on the portion that wasn’t used for training. The results show that the model VGG19 obtained an accuracy of 99.50% with a minimum data loss of 0.030, which outperforms VGG16, Inception V3, and ResNet50. This means that deep learning techniques prove to be effective in improving the precision of the cancer diagnostics process. The possibility for immediate incorporation into clinical practice is enormous, as these models help the pathologist automate their analysis over histopathological slides for quicker and faster diagnostics. This work has contributed to the body of knowledge in the field of medical imaging and paved the way for the integration of future AI-driven solutions in healthcare toward improved patient outcomes in the handling of breast cancer.