Sep 2026· International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences· Vol 14· 0 citations· 19 references
TL;DR
A lung cancer detection framework using a Convolutional Neural Network to classify chest CT images as cancerous or normal without relying on hand-crafted features is presented.
Abstract
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, largely because it is diagnosed at advanced stages when treatment options are limited. Conventional diagnosis relies on radiologists manually reviewing CT scans, a process that is time-taking and prone to error. This paper presents a lung cancer detection framework using a Convolutional Neural Network (CNN) to classify chest CT images as cancerous or normal without relying on hand-crafted features. The pipeline covers dataset acquisition from Kaggle, image preprocessing (resizing, normalization, noise reduction, and augmentation), automatic CNN-based feature extraction, and binary classification. The proposed CNN achieved 98.73% accuracy, 99% precision, 99% recall, an F-measure of 98.52%, and an error rate of 1.27%, outperforming four classical machine learning baselines — Decision Tree, Logistic Regression, Random Forest, and Naive Bayes — by a substantial margin.
A hybrid deep learning framework that pairs a 3D convolution neural networks (3D-CNN) with an recurrent neural networks (RNN) for modeling contextual dependencies across CT slices, capturing both shape-level structure of a nodule and how that structure evolves through the scan sequence.
Bokka Sireesha, K. Rajasekhar· International Journal of Sci...· 0 citations
Lung cancer accounts for the most cancer deaths in the world. While CT scans can detect lung cancer at an early stage, the manual reading of these scans is time-consuming and varies from doctor to doctor. To overcome these challenges, a framework is proposed based on deep learning techniques to classify CT chest scans...
Vinay Amin, Harshal A. Sanghvi, Riki H. Patel· International Workshop on Ar...· 0 citations
Background: Computed tomography (CT) scans and X-rays play a crucial role in diagnosing lung diseases. However, challenges such as imbalanced dataset and low image contrast can deter the performance of diagnostic models. To address these issues, convolutional neural networks (CNNs) offer a promising solution for respir...
Abdul Aleem, F. Siddique, G. Gilanie et al.· Sudan Journal of Medical Sci...· 0 citations
Background: Lung cancer remains one of the most common causes of cancer-related death globally, yet catching it early is still a significant challenge in clinical practice. Standard imaging tools such as CT scans and chest X-rays have real limitations: low-contrast lesions are easy to miss, and even experts can disagre...
Noshin Un Noor, J. Sarker, M. Riaj et al.· Bangladesh Journal of Medici...· 0 citations
Results indicate the potential of the proposed CAD framework to support radiologists as a second-reader tool in lung cancer screening, pending further external validation, and are competitive with, and in several respects exceed, recently reported approaches on the same dataset.
Anand G. Sharma, N. Kandoi, Priyanka V. Deshmukh· Egyptian Journal of Bronchol...· 0 citations
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