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The images were of size greater than 1000 pixels per dimension and the total dataset.\n\n- The dataset contained 5000+ X-ray images, labelled as showing symptoms of Pnuemonia or not.\n- The work includes\n  - Pre-processing of data.\n  - Laying down a Deep Convolutional Neural Network architecture from scratch.\n- The model showed a recall of 95% and a precision of 80%.\n  - In context of the problem statement, recall of the model plays a more crucial role for the successfull classifcation of images.\n- The final model architecture, loss functions and regularization steps have  been chosen after continous hyper parameter searches.\n \n\n### Insipiration\nAutomated methods to detect and classify human diseases from medical images.\n\n\n\n### Reading\n- [Batch Normalization](https://arxiv.org/abs/1502.03167)\n- [Xavier Initialization](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf)\n- [Dropout Regularization](https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf)\n- [Effective Backprop](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)\n- [How Does Batch Normalization Help Optimization?\n(No, It Is Not About Internal Covariate Shift)](https://arxiv.org/pdf/1805.11604.pdf)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaysharma096%2Fclassify-human-diseases-using-deeplearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakshaysharma096%2Fclassify-human-diseases-using-deeplearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshaysharma096%2Fclassify-human-diseases-using-deeplearning/lists"}