A Comprehensive Analysis of Hybrid ConvNeXt and Vision Transformer Architectures for Skin Cancer Classification: Evaluating Simpler vs. Advanced Models on the HAM10000 Dataset

Abstract

Skin cancer, with melanoma as its most lethal form, continues to challenge global healthcare systems, withanestimated2.5millionnewcasesreportedin2025alonebytheWorldHealthOrganization.Thisextensivestudyevaluates two innovative hybrid deep learning architectures for automated skin lesion classification using the HAM10000 dataset, comprising over 10,000 dermoscopic images across seven diagnostic categories. Architecture 1, a hybrid model integrating ConvNeXt for local feature extraction with Vision Transformer (ViT) for global context, achieves a commendable 94.5% accuracy.Architecture 2,an advanced iteration incorporating quantum-inspired feature selection and cross-attention fusion, elevates performance to 97.3% accuracy, 98.5% melanoma sensitivity, and a 0.98 AUC-ROC, establishing a new benchmark in diagnostic precision.The methodology encompasses detailed preprocessing techniques—normalization, augmentation (rotation, flipping, scaling, color jittering), and stratified data splitting (70% training, 15% validation, 15% testing)—alongside architectural innovations, hyperparameter optimization via grid search and five-fold cross-validation, andrigorousexternalvalidationon1,000diverseimages.Comparativeanalyseswithstate-of-the-artmodelslikeEfficientNet- B7 and ResNet50 reveal significant advantages, while discussions address clinical implications, limitations (e.g., datasetbias toward lighter skin tones), and future research directions, including diverse dataset integration, real-time optimiza- tion, and advanced augmentation strategies. This research underscores the transformative potential of hybrid AI in revo- lutionizing dermatological diagnostics.

Introduction

Background & Motivation:
Skin cancer—including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC)—is a growing public health issue, with melanoma being the deadliest despite its low incidence. Early detection is critical but challenging due to diagnostic subjectivity and limited access to specialists globally. AI, particularly deep learning, offers scalable solutions to improve accuracy and accessibility. This study focuses on hybrid models combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance melanoma detection and address healthcare disparities.

Hybrid Model Rationale:
CNNs excel at detecting local features like edges and textures in dermoscopic images, while ViTs capture global context through self-attention, improving recognition of complex lesion patterns. Two architectures are proposed:

• Architecture 1 is a streamlined CNN-based model optimized for efficiency and accuracy.

• Architecture 2 is a more advanced hybrid combining ConvNeXt (a CNN variant) with ViT, using quantum-inspired feature selection and cross-attention fusion to handle high-dimensional data and improve rare lesion detection.

Dataset and Objectives:
The study uses the HAM10000 dataset, containing over 10,000 annotated dermoscopic images across seven lesion types. The research aims to rigorously evaluate both architectures on metrics like accuracy, sensitivity, specificity, and ROC-AUC, focusing on melanoma detection. Generalizability will be tested on datasets from multiple continents, with an eye toward deployment on edge devices to improve diagnostic access in underserved regions.

Methodology:

• Data Preprocessing: Images are normalized, augmented extensively (rotations, flips, scaling, color jitter) to increase dataset variability and prevent overfitting. The dataset is split into training, validation, and testing sets with balanced classes and quality checks.

• Architecture 1: A CNN with five convolutional layers and three fully connected layers, leveraging ImageNet pre-trained weights and standard optimization techniques to classify images into seven classes.

• Architecture 2: A hybrid model combining a ConvNeXt CNN branch for high-resolution local features and a ViT branch for global context, fused via cross-attention layers to improve feature integration. It includes additional regularization to boost generalization.

Conclusion

Architecture2,integratingquantum-inspiredfeatureselectionandcross-attentionfusion,achieves97.3%accuracyand98.5% melanoma sensitivity, significantly outperforming Architecture 1 (94.5%) and benchmarks like EfficientNet-B7 [18].Validated through five-fold cross-validation and an external 1,000-image set (97.0% accuracy), it demonstrates robust generalization [14]. The hybrid ConvNeXt-ViT design effectively balances local and global feature extraction, offering a transformative diagnostic tool for early melanoma detection, potentially reducing mortality by 10-15% based on preliminary clinical projections [2].Its high computational requirements pose challenges for real-time use, necessitating optimization via pruning or edge deployment [25].Architecture1providesapracticalalternativeforresource-constrainedsettings,withalightweightprofilesuitableformobile platforms.Future research will prioritize dataset diversity through multi-ethnic image integration, address class imbalance with advanced augmentation (e.g., CycleGAN) [24], and develop lightweight models for edge devices, targeting a 50% reduction in inference time.This study advances AI-driven dermatology, paving the way for accessible, precise diagnostic solutions, with potential to revolutionize global healthcare delivery by 2030 [27].

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Copyright

Copyright © 2025 Ait Ameur Youssef, Elguerch Badr, Novaren Veraldo, Dommane Hamza. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.