Intelligent fusion-assisted skin lesion localization and classification for smart healthcare

With the rapid development of information technology, the conception of smart healthcare has progressively come to the fore. Smart healthcare utilizes next-generation technologies, such as artificial intelligence, the Internet of Things (IoT), big data and cloud computing to transform intelligently the existing medical system-making it more efficient, more reliable, and personalized. In this work, skin data are collected using dedicated hardware from mobile health units-working as nodes. The collected samples are uploaded to the cloud for further processing using a novel multi-modal information fusion framework, which performs skin lesion segmentation, followed by classification. The proposed framework has two main functional blocks: Segmentation and classification. In each block, we have a performance booster, which works on the principle of information fusion. For lesion segmentation, a hybrid framework is proposed, which utilizes the complementary strengths of two convolutional neural network (CNN) architectures to generate the segmented images. The resultant binary images are later fused using joint probability distribution and marginal distribution function. For lesion classification, a 30-layered CNN architecture is designed, which is trained on the HAM10000 dataset. A novel summation discriminant correlation analysis technique is used to fuse the extracted features from two fully connected layers. To avoid feature redundancy, a feature selection method “Regular Falsi” is developed, which down samples the extracted features into the lower dimensions. The selected features are finally classified using an extreme learning machine classifier. Five skin benchmark datasets (ISBI2016, ISIC2017, ISBI2018, ISIC2019, and HAM10000) are used to evaluate both segmentation and classification frameworks using average accuracy, false-negative rate, sensitivity, and computational time, whose results are impressive compared to state-of-the-art methods.