A Deep Learning Approach for Pulmonary Lesion Identification in Chest Radiographs

Radiography is a primary examination used to diagnose chest conditions, as it is fast, low cost, and widely available. If the physician cannot conclude de diagnosis with the radiography, a computed tomography scan may be required. However, this exam is expensive and has low availability, mainly in the public health system of developing countries and low-income locations, which can delay the treatment and cause complications to the patient’s health condition. Computer-aided diagnosis systems provide more resources for medical diagnostic decision-making, increasing the accuracy of the assessment of the patient’s clinical condition. The main objective of this work is to develop a deep-learning-based approach that performs an automatic analysis of digital images of chest radiographs to aid the detection of pulmonary nodules and masses, aiming to extract sufficient relevant information from the image, optimizing the initial phase of the diagnosis of lung lesions. The developed approach uses neural networks in a dataset of 8,178 annotated chest radiographs extracted from a public dataset. Half of it is of images annotated with “nodule” or “mass”, and the other half is of images with “no findings”. We implemented and tested convolutional neural networks and data preprocessing techniques to create a classification model. A model with five convolution layers that achieved 0.72 accuracy, 0.75 sensitivity, and 0.68 specificity. The proposed approach achieved results comparable to state of the art for lesion identification using limited computational power and can assist radiological practice as a second opinion, which can improve the rates of early diagnosed cancer.