Deep Learning-Based Skin Care Detection with Multi-method Explainability: Grad-CAM, Lime, and Occlusion Sensitivity

Abstract

Skin cancer is one of the most common malignancies worldwide, where early detection significantly improves treatment outcomes. While deep learning models show promise for automated skin lesion classification, their lack of interpretability limits clinical adoption. This study presents a comprehensive comparative analysis of three convolutional neural networks, ResNet-50, GoogLeNet, and SqueezeNet, for binary skin lesion classification (benign vs. malignant), integrating three explainable AI (XAI) methods (Grad-CAM, LIME, and Occlusion Sensitivity) to enhance clinical interpretability. We trained and evaluated these architectures on the Kaggle Skin Cancer dataset, which contains 2,637 dermoscopic images (1,440 benign, 1,197 malignant). Transfer learning employed ImageNet pre-trained weights with two-stage fine-tuning. Performance was assessed using accuracy, precision, recall, F1-score, specificity, and AUC-ROC metrics. ResNet-50 achieved the highest accuracy of 91.36% with an excellent AUC of 0.9721, demonstrating superior balanced performance. GoogLeNet achieved 88.94% accuracy with 73% fewer parameters, offering an optimal accuracy-efficiency trade-off. The proposed lightweight CNN, despite having the fewest parameters (1.2M), achieved 85.45% accuracy and a malignancy detection sensitivity of 92.7%, making it well-suited for screening applications. Training times ranged from 1.5 minutes (SqueezeNet) to 3 minutes 39 seconds (ResNet-50), demonstrating feasibility for resource-constrained settings. All XAI methods successfully generated clinically meaningful explanations, with models consistently focusing on lesion centers, color variations, and irregular borders. This study demonstrates that combining deep learning with XAI enables accurate and interpretable skin cancer detection. ResNet-50 is well-suited to well-resourced clinical settings, GoogLeNet offers balanced performance for resource-constrained deployments, and SqueezeNet enables mobile telemedicine applications with superior sensitivity.

ABSTRAK: Kanser kulit merupakan antara malignansi yang paling lazim di seluruh dunia, dan pengesanan awal terbukti dapat meningkatkan keberkesanan rawatan secara signifikan. Walaupun model pembelajaran mendalam menunjukkan potensi tinggi dalam pengelasan automatik lesi kulit, kekurangan kebolehinterpretasian telah mengehadkan penerimaan klinikal. Kajian ini membentangkan analisis perbandingan menyeluruh terhadap tiga rangkaian neural konvolusi, iaitu ResNet-50, GoogLeNet, dan SqueezeNet, bagi pengelasan binari lesi kulit (jinak vs. malignan), digabungkan dengan tiga kaedah kecerdasan buatan boleh jelas (XAI), iaitu Grad-CAM, LIME, dan Kepekaan Halangan, bagi menyokong interpretasi klinikal. Model dilatih dan dinilai menggunakan set data Kanser Kulit Kaggle yang mengandungi 2,637 imej dermoskopi, dengan menggunakan pembelajaran pindahan berasaskan pemberat pralatih ImageNet dan penalaan halus dua peringkat. Penilaian prestasi menggunakan metrik ketepatan, ketepatan ramalan, kepekaan, skor F1, pengkhususan, dan AUC-ROC menunjukkan bahawa ResNet-50 mencapai prestasi tertinggi dengan ketepatan 91.36% dan AUC 0.9721, manakala GoogLeNet menawarkan keseimbangan optimum antara ketepatan dan kecekapan dengan pengurangan parameter sebanyak 73%. SqueezeNet, walaupun paling ringan, mencapai kepekaan pengesanan malignan tertinggi sebanyak 92.7%, menjadikannya sesuai untuk aplikasi saringan dan teleperubatan mudah alih. Semua kaedah XAI berjaya menghasilkan penjelasan bermakna secara klinikal, dengan fokus konsisten pada pusat lesi, variasi warna, dan sempadan tidak sekata. Secara keseluruhan, kajian ini membuktikan bahawa penggabungan pembelajaran mendalam dan XAI membolehkan pengesanan kanser kulit yang tepat, boleh ditafsir, dan sesuai dalam pelbagai kekangan sumber klinikal.