• Divyansh Dadhwal
• Dr. Avni Sharma

Lung cancer is still one of the deadliest and frequently diagnosed malignancies in the world, and patient survival depends heavily on early identification. Due to human subjectivity, manual CT scan examination and biopsy are two time-consuming and error-prone traditional diagnostic techniques. Image processing methods have greatly increased the speed and accuracy of medical diagnosis in recent years, particularly for the identification of cancer. Advanced image processing methods can be implemented on a comprehensive platform with MATLAB, a high-level language and interactive environment for numerical computation and visualization. This study uses MATLAB-based image processing to investigate a methodical approach for early lung cancer identification. Image acquisition, pre-processing, segmentation, feature extraction, and classification are some of the steps in the methodology. The lung CT images are enhanced and segmented using methods such region-growing, median filtering, histogram equalization, and morphological processes. To find possible malignant nodules, characteristics including texture, shape, and intensity levels are taken out and examined. Lastly, to increase detection accuracy, machine learning classifiers such as Support Vector Machines (SVM) are employed. The study shows how well MATLAB works to automate and optimize the diagnosis of lung cancer, which may lower false positives and increase the rate of early detection.

"Detecting Lung Cancer Using Image Processing Techniques with Gabor filter and SVM classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.i270-i274, May-2025, Available :http://www.jetir.org/papers/JETIR2505926.pdf

"Detecting Lung Cancer Using Image Processing Techniques with Gabor filter and SVM classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppi270-i274, May-2025, Available at : http://www.jetir.org/papers/JETIR2505926.pdf