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These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model.\n\u003ctable\u003e\n    \u003ctr\u003e\n    \u003cth colspan=\"2\"\u003eTesting Result\u003c/th\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003cth\u003eTest Implementation Name\u003c/th\u003e\n        \u003cth\u003eTest Accuracy\u003c/th\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n       \u003ctd\u003eCNN Implementation - 1\u003c/td\u003e\n        \u003ctd\u003e0.8780487775802612\u003c/td\u003e\n    \u003c/tr\u003e\n        \u003ctd\u003eCNN Implementation - 2\u003c/td\u003e\n        \u003ctd\u003e0.9451219439506531\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n        \u003ctd\u003eTransfer_Learning_Implementation - 3\u003c/td\u003e\n        \u003ctd\u003e0.957317054271698\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n    \u003ctd colspan=\"2\"\u003e\u003cimg src=\"https://static.javatpoint.com/tutorial/artificial-neural-network/images/artificial-neural-network4.png\"\u003e\n    \u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/table\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoyalshaji135%2Fcnn-implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoyalshaji135%2Fcnn-implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoyalshaji135%2Fcnn-implementation/lists"}