Ankita Roy,Atul Garg,



Deep Learning Predictive Models,Diseases,Lung Cancer,Pneumonia,Tuberculosis,


The healthcare sector is characterized by a vast amount of information and holds significant potential for improvement through the integration of state-of-the-art technologies. Deep learning models have been regarded as being particularly ideal since they can efficiently handle and analyze enormous amounts of data, allowing them to attain the highest possible level of accuracy. This study aims to conduct a comprehensive analysis of various deep learning models by comparing their performance on different datasets. Additionally, it will focus on the practical application of the VGG-16 and AlexNet models specifically on the ChestX-ray14 dataset. The evaluation of the accuracy of numerous deep-learning models is conducted to assess the efficacy and performance of such models. Among the array of models available, the Genetic Deep Learning Convolutional Neural Network (GDCNN), DenseNet-201, and Convolutional Neural Network (CNN) have emerged as top contenders, showcasing superior performance and robustness. The GDCNN achieved an accuracy of 98.84 percent, and DenseNet-201 exhibited an accuracy of 97.2 percent. Notably, the CNN outperformed the other models with an accuracy of 99.39 percent. The incorporation of a larger dataset, the addition of more convolutional layers to the CNN, and image segmentation techniques may enhance the overall performance and accuracy levels.


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