Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.

Introduction: Skin lesion segmentation and classification is an active research area in medical imaging for the large number of reported deaths in the recent years. Early diagnosis of skin cancer is essential to decrease the death rate and increase life expectancy.

Objectives: Several artificial intelligence (AI) based techniques have been introduced in the literature for the diagnosing skin cancer; however, due to challenge of imbalanced datasets, irregular lesion shape, presence of lesions on boundary regions, and selection of inappropriate model selection, the performance of AI model is highly impacted. Therefore, in this work our main objective is to propose a fully automated deep framework for skin lesion segmentation and classification with more efficient and effective way.

Method: This work proposes a novel framework for segmenting and classifying skin lesions using improved ResNet20-DeepLabV3+ and MAKNet100 deep models. In the segmentation task, a ResNet20 architecture is designed as a backbone of DeepLabV3+. In the designed ResNet20 architecture, a few grouped convolutional layers are added with smaller filter sizes to extract more insight information. A new MAKNet100 model is proposed in the classification task based on the network-level fusion of two custom models. The proposed network has few parameters and can extract more information about the lesion images. The proposed model is trained and further analyzed using the GradCAM explainable artificial technique (XAI) as a black box interpretation. Features are extracted from the self-attention layer and passed to classifiers for the final classification.

Results: The experimental process of the proposed framework is performed on HAM10000, ISIC-2018, ISIC-2019, and ISBI-2020 datasets with an accuracy of 90.5 %, 88.9 %, 84.5 %, and 96.35 % respectively and the highest obtained dice score on ISIC-2018 and HAM10000 is 94.63 and 96.69 % respectively.

Conclusion: The proposed framework obtained improved accuracy and precision rates for skin lesion segmentation and classification on these datasets. Moreover, the ablation study and comparison with existing techniques show the proposed framework's dominance.