Integrating Auxilary Cross Entropy In The Inceptionv3 Network For Automatic Detection And Classification Of Lung Disease

Abstract

Automated Detection And Classification Of Lung Diseases From Medical Imaging Data Play A Pivotal Role In Early Diagnosis And Treatment Planning. In This Study, We Propose The Integration Of Auxiliary Cross Entropy In The Inceptionv3 Network To Enhance The Accuracy And Efficiency Of Lung Disease Detection And Classification. By Augmenting The Inceptionv3 Architecture With Auxiliary Losses, Our Aim Is To Improve The Network's Ability To Learn Discriminative Features For Differentiating Between Various Lung Disease Patterns. Through The Utilization Of Deep Learning Techniques And Auxiliary Supervision, We Seek To Develop A Robust System Capable Of Accurately Identifying And Classifying Different Lung Diseases From X-Ray Images. Based On Various Models Experiments, The Results Displayed Both Cross Entropy Optimized Inception V3 Methods Have Outperformed Other Baselines Models By A Minimum Of 1% Accuracy For Example In Inception V3 Baseline, By A Maximum Of 16% In VGG19 Baseline Model. Therefore. We Can Conclude That The Proposed Cross Entropy Optimized Inception V3 Strong Enough To Identify Lung Disease. Therefore, The Experimental Results Demonstrate The Efficacy Of Our Approach In Enhancing The Performance Of The Inceptionv3 Network For Automatic Detection And Classification Of Lung Diseases, Paving The Way For More Precise And Reliable Medical Image Analysis In The Field Of Pulmonary Healthcare.