Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

Sep 24, 2026

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Lung cancer’s high mortality makes early detection critical, yet accurate identification of nodules is difficult because lung focal opacities visually resemble the trachea, vessels, and surrounding tissues. A computational predictive pipeline is developed to detect and classify lung nodules using CNN-based segmentation on the LIDC dataset, followed by extraction of optimal features including histogram of oriented gradients, local binary patterns, and geometric descriptors. Classification results show that a support vector machine achieves the strongest performance, reaching 97.8% accuracy with 100% sensitivity, 93% specificity, and a 6.7% false positive rate.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Introduction

Lung cancer background and risk factors

Anatomy of human lungs and nodules

Types of nodules: micronodules, focal opacity, mass

Imaging modalities and detection challenges

Proposed method: segmentation and feature extraction

Optimal features: HOG, LBP, and geometric features

Classification results and evaluation

Hindawi Computational Intelligence and Neuroscience Volume 2023, Article ID 6357252, 16 pages [https://doi.org/10.1155/2023/6357252](https://doi.org/10.1155/2023/6357252) Research Article Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection Muhammad Ashar Javed , 1 Hannan Bin Liaqat,2 Talha Meraj ,3 Aziz Alotaibi ,4 and Majid Alshammari5 1 Department of Information Technology, University of Gujrat, Gujrat, Pakistan 2 Department of Information Technology, Division of Science and Technology University of Education, Township Campus Lahore, Lahore, Pakistan 3 Department of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt, Rawalpindi 47040, Pakistan 4 Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia 5 Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia Correspondence should be addressed to Muhammad Ashar Javed; [asharjaved255@gmail.com](asharjaved255@gmail.com) Received 14 July 2022; Revised 7 September 2022; Accepted 26 September 2022; Published 26 July 2023 Academic Editor: Dalin Zhang Copyright © 2023 Muhammad Ashar Javed et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient’s survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassifcation of lung nodules. Terefore, correct identifcation and classifcation of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, atfrst, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classifcation include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. Te results shown that support vector machines performed better in identifying the nodules than other classifers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specifcity of 93%, and false positive rate of 6.7% . 1. Introduction Cancer professes a great threat worldwide to human health. Among all other types of cancers, lung cancer has the highest death rate. According to the world health report, about 8.2 million deaths occur per year due to cancer and 1.69 million of which are due to lung cancer [1]. Te survival rate in lung cancer is very low than other cancers. In spite of the advancement in medical treatments of lungs cancer, its fveyear survival rate still fuctuates from 4% to 17%, but if the lung cancer is identifed at its early stages, the survival rate can be improved [2] . Te key is to identify the exact location of nodules. Te lung malignancy is caused by abnormal growth of cells in lungs tissues. Risk factors that cause the cancer to happen are biological reactions, chemical reactions, and smoking. [3] . Te human lungs are pyramid in shape paired organs (left and right lungs) that are connected through trachea known as a windpipe. Te trachea is further connected to two bronchi (airway in respiratory systems) that holds both the paired organs together and regulates oxygen. Tere are mainly two bronchi: the one

FAQs

Why is identifying lung focal opacity difficult for radiologists?

Because lung nodules can have visual similarity in shape and intensity to the trachea, vessels, and nearby tissues, making detection challenging.

What dataset and step are used to locate nodules in the proposed framework?

The framework performs semantic segmentation on images from the LIDC (Lung Image Database Consortium) dataset to identify nodules.

Which classifier performed best and what were its key metrics?

A support vector machine performed best, achieving 97.8% accuracy with 100% sensitivity, 93% specificity, and a 6.7% false positive rate.

Sep 24, 2026

Upload by Sarah