Skin Cancer Detection: Image Classification Using CNN Architectures with CLAHE

Shajahan, Syed Munazir (2024) Skin Cancer Detection: Image Classification Using CNN Architectures with CLAHE. Masters thesis, Dublin, National College of Ireland.

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Abstract

Skin cancer is a significant health problem that is common and can be improved if recognized early and avoided. In this paper, the authors examine Contrast Limited Adaptive Histogram Equalization (CLAHE), which is applicable for changing the images to enhance their contrast, to improve the efficiency of various Convolutional Neural Networks (CNNs) in detecting various skin lesions. They used the HAM10000 dataset with 10,015 skin images for testing the three implemented CNNs ResNet, DenseNet, and EfficientNet with raw and CLAHE applied images. The first case results showed that CLAHE works well with models in detecting skin features and increases classification accuracy. The DenseNet turned out to be the one with the highest performance of 77% accuracy and recall when given CLAHE-enhanced images, whereas ResNet came up to 75% accuracy; however, the precision revealed a poorer performance compared to the others for EfficientNet. The study highlights the good use of CLAHE as the best means of improving the detection of skin cancer by means of CNN models, namely DenseNet for most effective results. New image enhancement methods and larger datasets will be discussed in future work.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Singh, Jaswinder UNSPECIFIED
Uncontrolled Keywords:	Skin cancer; CNN; CLAHE Technique; Image Classification; Deep Learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Life sciences > Medical sciences > Pathology > Tumors > Cancer R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 14:49
Last Modified:	04 Sep 2025 14:49
URI:	https://norma.ncirl.ie/id/eprint/8802

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