An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification

TL;DR

This paper presents an end-to-end deep CNN model for classifying interstitial lung disease patterns, achieving high accuracy and outperforming existing pre-trained models on a large CT scan dataset.

Contribution

The paper introduces a novel deep CNN architecture specifically designed for ILD classification, demonstrating superior performance over pre-trained models.

Findings

Achieved 99.09% accuracy in ILD classification

Outperformed pre-trained CNNs in precision, recall, and F score

Validated on a large dataset of 21,328 image patches

Abstract

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9%, outperforming three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.