Investigating Deep Learning Techniques for Automization of X-Ray Image Classification in Healthcare
Keywords:
Machine Learning; Healthcare; Deep Learning; Transfer Learning; Heart Disease (HD); Cancer.Abstract
As a result of technological advancements, machine learning is emerging as a cutting-edge trend in healthcare that aids medical professionals in making decisions and enhances the precision of diagnoses. Investigators are seeking technological solutions to support healthcare providers in their daily tasks. X-ray images are most widely utilized diagnostic imaging methods in healthcare due to low cost and availability. The use of deep learning for the automatic classification of x-ray images can be extremely beneficial in situations where there is a inadequacy of available experts or can assist experts in obtaining a second opinion. The researchers investigated popular CNN architectures, including VGG, RESNET, DENSENET, EFFICIENTNET, and MOBILENET, and optimized them using two datasets of x-ray images. First dataset focuses on heart disease, whereas the second one pertains to breast cancer. Among all models, the MobileNet achieved the highest performance across both datasets when assessing metrics like accuracy, F1 score, recall, and precision..
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