Deep Learning Neurons in Medical Insight
Revolutionizing Image Analysis for Disease Prediction and Diagnosis
Abstract
Extensive research in the field of medical health systems opens prospects for implementing IT systems with the latest innovations. These innovations focus on the efficient use of medical systems, including automated health diagnostics. In healthcare, the focus is on predicting cancer, its various forms and its effects on various organs. Considered difficult to treat, cancer is one of the most aggressive forms, often occurring in advanced stages, making effective treatment difficult. Considering this, medical research is seeking to implement automated systems to determine cancer stages, allowing for more accurate diagnosis and treatment. Deep learning is becoming a key area, expanding into medical imaging, automating diagnostic processes using technologies such as CT/PET systems. Prediction of cancer spread is carried out using threshold parameters as markers. The research direction of this dissertation focuses on the area of medicine covering the prognosis of various forms of cancer.
The literature review includes various articles focusing on the application of deep learning in a medical context, with a special focus on breast cancer. Topics discussed include predicting response to chemotherapy in triple-negative breast cancer, automated detection of liver metastases from CT images, assessing response to immunotherapy in lung cancer, and predicting the clinical benefit of adjuvant chemotherapy in hormone receptor-positive breast cancer.
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