International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Medical Image Processing and Machine Learning Based On Lung Cancer Detection Using CT Scan Images |
|---|---|
| ABSTRACT | Lung cancer is one of the major causes of cancer-related deaths, and early detection is important for better treatment outcomes. Computed Tomography (CT) scans provide detailed information about lung structures and are commonly used to identify abnormal tissues. However, manually analyzing a large number of medical images can be time-consuming and may lead to variations in the diagnostic process. This paper presents a medical image processing and machine learning-based framework for detecting lung cancer from CT scan images. The proposed approach combines image preprocessing, frequency-domain analysis, feature extraction, and supervised machine learning. CT images from a publicly available Kaggle dataset are first resized and converted to grayscale. Median filtering is then applied to reduce noise while preserving important image structures. Fast Fourier Transform (FFT) is used to convert the processed images into the frequency domain and extract frequency-based features. The extracted features are classified using four machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). An 80:20 training-testing split is used for evaluation. The models are compared using Accuracy, Precision, Recall, F1-Score, ROC curves, and Area Under the Curve (AUC). Confusion matrices and performance comparisons are also used to examine the classification results. The experimental results demonstrate how conventional image processing and machine learning techniques can be combined for computer-aided lung cancer analysis. The performance of the selected classifiers is compared to determine their effectiveness on the given CT scan dataset. |
| AUTHOR | SAFIYA HAALA, KRUTHIKA VT Student, Department of MCA, CMR University, Bhuvanagiri, OMBR Layout, Bengaluru, India Assistant Professor, SSCS, OMBR, CMR University, Karnataka, India |
| VOLUME | 188 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1409010 |
| pdf/10_Medical Image Processing and Machine Learning Based On Lung Cancer Detection Using CT Scan Images.pdf | |
| KEYWORDS | |
| References |
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