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Over the last few years of when deep learning systems have been introduced into medical imaging procedures they are uncorrupted. They should be commended as the best ways of revolutionizing diagnosis processes and improving healthcare outcomes. This report focuses on detailed examination of deep learning for application to automated medical diagnostic, which is one of the most cutting-edge technology at present. Diverse input was the is collected by different kinds of modalities like X-ray, CT scan, MRI, and ultrasound. Also, this input modality is preprocessed to train and validate the proposed deep learning model. The network's architecture was appositely chosen, and the training strategy worked sense prep, which was made to improve the achieved success, included to the method of tuning and cross-validity was used. The qualitative analysis of accuracy, sensitivity and specificity was very good, which is a proof for a better performance of the novel method compared to the baseline ones. Furthermore, the qualitative data analysis and interpretability led to fundamental discussions on the model's performance and the decision-making procedure. Moreover, the model is of good generalizability, maintaining high accuracy on different datasets of validation group, therefore, it is predictive for clinical uses. Overall, this paper confirms wide application of deep learning methods in the field of machine learning-based medical diagnosis and highlights need of further research and development in the area of high tech ones which at present are highly dynamic by nature.
Neelaveni et al. (Fri,) studied this question.