Neural Network Optimized Medical Image Classification with a Deep Comparison

Abstract

Clinical care and educational assignments both heavily rely on the categorization of medical images. However, the performance of the conventional approach has peaked. Additionally, employing them requires extensive time and effort to extract and choose categorization characteristics. An innovative machine learning technique called the deep neural network has demonstrated its potential for many categorization problems. On several picture classification tasks, the convolutional neural network stands out with the greatest results. Clinical care and therapy are greatly aided by accurate medical picture classification. For instance, the analysis X-ray is the best method for diagnosing pneumonia, which kills over 50,000 people annually in the US, but identifying pneumonia from chest X-rays requires qualified radiologists, which can be difficult and expensive in some areas. Medical image categorization has traditionally used standard machine learning techniques like support vector machines (SVMs). The main motive of the authors seem to be optimizing the medical image classification using Deep learning neural networks such as DNN, ANN and CNN.