Accurate Neoplasm Diagnosis With Comprehensive Machine Learning and Deep Learning Approaches

Abstract

Machine learning (ML) and deep learning (DL) technologies break in the new line of medical diagnostics that offer tremendous benefits in term of accuracy, efficiency and predicting the outcome of disease, specifically neoplasm detection and classification. In this work, a thorough study of various deep learning and machine learning techniques is presented, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Decision Trees, Support Vector Machines (SVMs) as well as other ones, that are applied to the diagnostic task of neoplastic diseases. By means of number of experiments and comparison studies, we do research work to evaluate the extend of these computational methods to change the etiopathogenesis of the disease. The time of diagnosis can be significantly reduced due to the dictation of diagnosis and the detection and classification of the disease become more precise. The integration of ML and DL technologies within clinical settings may not only enhance the diagnostic capabilities but also drive the development of customized treatment plans suitable to a patient's specific characteristics of their disease pathology. Also, our study report on the adaptability of the systems in which they can constantly update their diagnostic performance by taking a new stock of data. © 2024 IEEE.