One or Two Research Paper Digests

In this paper , the authors explore the use of Deep Convolutional Neural Networls (DCNN) in classifying Tuberculosis (TB) in chest radiographs. One of the advantages of deep learning is its ability to excel with high dimensional datasets, such as images, which can be represented at multiple levels.  Dataset Four deidentified HIPAA-compliant datasets were used in this study that were exempted from review by the institutional review board, which consisted of 1007 posteroanterior chest radiographs. DCNN Models and Training AlexNet and GoogLeNet models, including pre-trained (on ImageNet from Caffe Model Zoo) and untrained models were used in the study. It was found that the AUCs of the pretrained networks were greater. The following solver parameters were used for training: 120 epochs; base learning rate for untrained models and for pretrained models, 0.01 and 0.001, respectively with stochastic gradient descent.  Both of the DCNNs in this studied used dropout or model regularizati

In this paper , the authors present a deep learning model to detect disease from chest x-ray images. A convolutional neural network (CNN) is trained to detect the disease names. Recurrent neural networks (RNNs) are then trained to describe the contexts of a detected disease, based on the deep CNN features. CNN Models used and Dataset CNNs encode input images effectively. In this paper, the authors experiment with a Network in Network (NIN) model and GoogLeNet model. The dataset contains 3,955 radiology reports and 7,470 associated chest x-rays. 71% of the dataset accounts for normal cases (no disease). The data set was balanced by augmenting training images by randomly cropping 224x224 images from the original 256x256 size image. Adaptability of Transfer learning Since this boils down to a classification problem on a small dataset, transfer learning is a technique that comes to our mind. The authors experimented this with ImageNet trained models. ImageNet trained CNN we