DJCCMT

Delta Journal of Computing, Communications and Media Technologies

ISSN:3092-8478

Advancing research and innovation at the intersection of computing technology and media. A publication of Southern Delta University, Ozoro.

Delta Journal of Computing, Communications and Media Technologies(DJCCMT) is an open access double-blind peer reviewed and refereed Journal that brings together reasoned thoughts, research, and industry practice in areas of Computing, Artificial Intelligence, Robotics, System Engineering, Data Science, Analytics, Embedded Systems, Information and System Security, Media Studies, Communication Technologies, Information Science, Library Science, Educational Technologies, Applied Computing, and related disciplines in a reader-friendly format. The Journal is published online monthly with print version issue in February, May, August and November.

Delta Journal of Computing, Communications and Media Technologies

Volume 1 · Issue 1 · December 2024
Title of Paper

CLAHE: AN IMPROVED LUNG CANCER DIAGNOSTIC AND CLASSIFICATION MODEL

Abstract

This article introduces a cancer diagnostic model utilizing Contrast Limited Adaptive Histogram Equalization (CLAHE) in conjunction with Convolutional Neural Networks (CNN) and X-ray images to detect lung cancer. Medical image processing plays important during the diagnosing of lung cancer, assisting doctors in making accurate diagnoses and treatment decisions. Cancer of the lung is considered one of the deadliest diseases, and early detection can save many lives. Given its severity, a reliable diagnostic model is essential for identifying the nature of the cancer of the lung found in patients. During the preprocessing stage, Adaptive Median Filtering is applied to remove speckle and Gaussian noise from the X-ray images, enhancing image quality with the aid of CLAHE. The model aims is to identify any type of cancer detected as either Cancerous or Non-Cancerous: if no tumor is detected, the result is classified as “Non-Cancerous,” while the absence of a tumor is categorized as “Cancerous.” Experimental results indicate that the model presented a detection accuracy of 90.77%, with a precision of 86.65% and a recall/sensitivity of 95.31%. The framework was designed using the C# platform and employs EMGU.

Authors

Kizito Eluemunor Anazia, Emmanual Obiajulu Ojei,Friday Erife Eti,Ogheneochuko Ubrurhe,Kasimir Ebejale Ikueobe,Vivian Onyinye Okeke

Keywords

CLAHE, Convolutional Neural Networks, Adaptive Median Filtering, K-Means Clustering Algorithm Cancerous and Non-Cancerous.

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