Chest disease radiography in twofold: using convolutional neural networks and transfer learning

• DOI: 10.1007/s12530-019-09316-2
• Publication Date: 2019-11-28T20:02:31Z
• Authors: Prakash Choudhary; Abhishek Hazra
• Keywords: Softmax function; Transfer of learning; Pooling; Benchmark (surveying); Feature (linguistics); Feature Engineering; Scratch; Contextual image classification; Feature Learning

Computer-aided diagnosis and design in the medical province is an exciting domain owing to drastic growth in Medical images. Earlier handcraft feature learning techniques failed to achieve the targeted result in practical aspects. In this paper, we have adopted a deep learning artifice to reduce the semantic gap which exists between the low-level information captured by imaging devices and high-level information preserved by a human. The proposed work has twofold: first, we propose convolutional neural network architecture consisting of five types of layers, convolutional layer, an activation layer, Pooling layer and Fully connected layer followed by a Softmax layer which gives the probability of the output for every genre. The second contribution towards this paper is to find the solution of an unsolved problem in medical image analysis: “Uses of a pretrained model with adequate fine-tuning to eliminate the extra effort of making a new CNN architecture from scratch”. To address this puzzle, we employed a pretrained VGG-16 model (a famous CNN architecture trained on Image Net dataset) to train the same dataset. Grad-CAM is used for visualizing the model performance with respect to a test image. The proposed methods are evaluated on famous publicly available NIH dataset called Chest X-Ray 14 and have created a new benchmark performance that has achieved state-of-the-art results 83.671% (scratch CNN) and 97.81% (transfer learning), which are much higher as compared to the other methods. Moreover, we also introduce in-depth comparison with the current existing works.