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Among them, EfficientNet-B7 (which achieved highest accuracy over ImageNet dataset) is used here to solve a classsification problem, detection of Pneumonia in Chest X-ray Images. Without any augmentation, 100% precision can be achieved. \u003cbr /\u003e\n\n## Database\nKermany D., Goldbaum M., Cai W. Large dataset of labeled optical coherence tomography (OCT) and chest X-Ray images 2018, 172, 1122–1131. Cell. 2018;172:1122–1131. doi: 10.1016/j.cell.2018.02.010.\nhttps://data.mendeley.com/datasets/rscbjbr9sj/3 \u003cbr /\u003e\n```\nUnzip ZhangLabData.zip\nCopy it to \"./data/\" directory\n```\nTraining Dataset:\n```\nTotal Images: 5233\nNormal Healthy Person: 1349\nPnumonia Patients: 3884\n```\nTesting Dataset:\n```\nTotal Images: 624\nNormal Healthy Person: 234\nPnumonia Patients: 390\n```\nValidation Dataset is created from Training Dataset for calibrating Hyperparameters.\n\n## Codebase\nDriver Program\n```\ntrain.py = runs training session\ntest.py = runs testing session\nfolder_to_csv.py = lists files in a folder\nmerge_csv.py = merges contents in CSV files\naugmentation.py = creates augmented dataset\n```\nSetting File\n```\nsettings.json = contains hyperparameters\n```\nUtility Classes\n```\n_datagen.py = data generator for deep learning session\n_train_test.py = runs deep learning session\n```\nEfficientNet by Luke Melas-Kyriazi\nhttps://github.com/lukemelas/EfficientNet-PyTorch\n```\n./efficientNet/\n```\n\n## Result\nPlain Data (No Augmentation)\n```\nAccuracy = 87.98%\nPrecision = 100%\nRecall = 83.87%\nF1 Score = 91.23\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fani717%2Fpneumonia_detection_effecientnet_b7","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fani717%2Fpneumonia_detection_effecientnet_b7","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fani717%2Fpneumonia_detection_effecientnet_b7/lists"}