Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

Mehedi Masud,M Shamim Hossain,Ghulam Muhammad,Hesham Alhumyani,Saleh Ibrahim,Sultan S Alshamrani,Omar Cheikhrouhou

doi:10.1155/2020/8893494

Abstract

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis.

Highlights

Due to the advancement of sophisticated machine learning algorithms, mobile computing, wireless communications [1, 2], and cognitive computing [3, 4], the healthcare industry is booming in recent years
One of the major driving forces behind the rise of the smart healthcare industry is the invention of deep learning algorithms in machine learning domain [7]
We proposed a convolutional neural network- (CNN-) based pulmonary nodule detection and classification system

Summary

Introduction

Due to the advancement of sophisticated machine learning algorithms, mobile computing, wireless communications [1, 2], and cognitive computing [3, 4], the healthcare industry is booming in recent years. The traditional healthcare industry is gradually shifting towards the smart healthcare industry [5]. The smart healthcare enables patients to have their health problems diagnosed sitting at their homes, to get the prescription and advice online, and thereby to save time for communication and getting an appointment [6]. One of the major driving forces behind the rise of the smart healthcare industry is the invention of deep learning algorithms in machine learning domain [7]. Deep learning has brought about a paradigm shift to machine learning. For the last ten years, it was used in numerous applications for signal and image processing, including medical signals or images [8,9,10]

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