A Lightweight Pruned DCNN Model with XAI for Skin Cancer Classification

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creativework.keywordsDCNN model, skin cancer, cancer detection
dc.contributor.advisorDr. Sifat Momen
dc.contributor.authorMEHEDI HASAN
dc.contributor.authorMAHIR SHAHRIAR ABIR
dc.contributor.authorABU TAYEB MOHAMMAD AYON
dc.contributor.id2011425042
dc.contributor.id2013640042
dc.contributor.id2012547042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2026-07-28
dc.date.accessioned2026-07-28T07:53:40Z
dc.date.available2026-07-28T07:53:40Z
dc.date.issued2024-08
dc.description.abstractSkin cancer is one of the most destructive types of cancer due to its immediate arrival and the potential for rapid spread. Sometimes, traditional diagnostic methods don’t react immediately because they need dermatologist expertise, some automation tools and time. This study explores one of the areas of artificial intelligencedeep learning, specifically Convolutional Neural Networks (CNNs), to enhance the efficiency and accuracy of skin cancer diagnosis. In this article, we introduce a lightweight pruned deep Convolutional Neural Network (dCNN) based method for skin cancer detection. With significantly lower trainable parameters, it surpasses recent state-of-the-art methods and shows competitive performance against 11 CNN-based pre-trained models, including Densenet121, DenseNet201, EfficientNetB2, InceptionV3, MobileNetV2, ResNet50, ResNet50V2, ResNet101V2, VGG16, VGG19, Xception. The proposed model demonstrates an accuracy of 98.07% with a notable precision of 98.15%, recall of 98.07%, and an F2 score of 98.02%. Even model interpretability perfectly works with Grad-Cam++. The proposed pruned model not only gets higher accuracy but also reduces computational cost, making it suitable for deploy on any device.
dc.description.degreeUndergraduate
dc.identifier.cd600000300
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1715
dc.language.isoen_US
dc.publisherNorth South University
dc.rights@ NSU Library
dc.titleA Lightweight Pruned DCNN Model with XAI for Skin Cancer Classification
dc.typeProject
oaire.citation.endPage53
oaire.citation.startPage1
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