Chest medical image classification using deep network.

Abstract

Human lung which is among the most important parts in human body is facing mortal diseases especially after the COVID-19 pandemic. The scientific world is rapidly developing the health-care field to face these disorders and save millions of lives all around the world. The primary objective was to find aprecise an defficient strate gyf ort heaccura tea ndear lydetecti onand classificatio no flun gdiseases .T oachiev ethi sgoal ,w euse dth epowe ro ftwo essential medical imaging techniques: computerized tomography (CT-scan) and X-ray imaging. Additionally, we employed three deep learning models: Inception-v3, ResNet, and DenseNet, coupled with two distinct classification; binary classificatio nan dmulti-clas sclassificatio n.O urresear chjourn eystarted with binary classification ,focusin go ndistinguishin gbetwee nCOVID-1 9and non COVID-19, using both CT-scan and X-ray datasets in total of 17,599, all three models delivered outstanding results, with the highest accuracy reaching an impressive accuracy of 96%, achieved by DenseNet using CT-scan images. These results underscore the potential of deep learning in helping healthcare professionals with highly accurate disease classification .Shiftin gt oth emulti- class classificatio ndictate db yth enee dfo r amor ecomprehensiv ean drealistic approach to diagnosing and identifying a wide range of medical conditions in clinical practice and research. The new class added to COVID-19, non COVID-19 is: Community-acquired pneumonia (CAP), in total of 17,104 CT- scan images,and using the same models we challenged the system using different splitting data ratios. Through a series of experiments and evaluations, our system achieves an overall accuracy of 98% in classifying chest images across multiple categories, using DenseNet model and the 80:10:10 splitting ratio. The results showcase the significan tpotentia lo fdee plearnin gi nassisting healthcare.