Predictive Modelling for Medical Image Analysis Using Deep Learning Techniques

Abstract

Recent advancements in healthcare for the prediction of autosomal diseases have led to the usage of deep learning algorithms in analysing medical images. Autosomal diseases are an extensive group of illnesses that range from cardiovascular diseases to specific types of tumours. Leveraging Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can forecast models accurately and then very efficiently, but it has limitations in finding autosomal chromosomes. The autosomal chromosome uses advanced deep-learning algorithms to analyse a database of medical images, including MRI and CT scans, to predict the onset and progression of inherited disorders. Predictive accuracy is maximized by the usage of data preparation, model training, and learning strategies. The prognosis and early finding of autosomal diseases can be greatly enhanced by algorithms for timely intervention and customized treatment for patients. Further integration of analysing medical images can give more patient care and improve disease prediction results, particularly in the case of autosomal disorders and diagnosis.