DETECTION OF MULTIPLE DISEASES FROM CHEST X-RAY IMAGES USING VARIOUS DEEP LEARNING APPROACHES

Registration ID: IJNRD_208872

Published ID: IJNRD2311166

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Pritpal Singh

A vital field of study is multiple illness detection, which attempts to enhance healthcare outcomes by simultaneously recognizing many diseases in individuals. A single illness is identified at a time in the conventional method of disease identification, which is time-consuming, expensive, and usually leads to missed diagnoses. Because of developments in machine learning and artificial intelligence, many illness detection models have been created that can aid in the accurate and speedy diagnosis of multiple diseases. These models use information from a variety of sources, including medical records, lab test results, and imaging data, to discover trends and forecast the risk of certain illnesses. Pneumonia, COVID19, and tuberculosis are just a few of the diseases and disorders that are analyzed, classified, and predicted using the many models that are put forward in this study and research. The public dataset utilized for illness categorization and prediction comprises 7135 chest X-ray images that have been divided into four categories: normal, COVID19, pneumonia, and tuberculosis. Since the dataset being utilized is imbalanced, we gave Class weights to it in order to reduce overfitting while training deep learning models. Several Machine Learning and Deep Transfer Learning Models, including Convolutional Neural Network, Inception-V3, EfficientNetB0, Resnet, and VGG-16, have been deployed for the prediction and classification of multiple diseases. The accuracy of the CNN deep learning model is 82%, the accuracy of Inception-V3 is 86%, EfficientNetB0 is 91%, and Resnet15V2 is 92%. On top of that, the VGG-16 deep learning model outperformed competing models to reach accuracy of 95.3%, with precision, recall, and F1-Score at 96%, 97%, and 98%, respectively.

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Pritpal Singh (November-2023). DETECTION OF MULTIPLE DISEASES FROM CHEST X-RAY IMAGES USING VARIOUS DEEP LEARNING APPROACHES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), b517-b526. https://ijnrd.org/papers/IJNRD2311166.pdf

Volume 8 Issue 11, November-2023

Pages : b517-b526

Paper Reg. ID: IJNRD_208872

Published Paper Id: IJNRD2311166

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Gurdaspur, Punjab, India

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