Ahmad Alassaf^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 2773-2789, 2023, DOI:10.32604/csse.2023.035899

Abstract Intelligent diagnosis approaches with shallow architectural models play an essential role in healthcare. Deep Learning (DL) models with unsupervised learning concepts have been proposed because high-quality feature extraction and adequate labelled details significantly influence shallow models. On the other hand, skin lesion-based segregation and disintegration procedures play an essential role in earlier skin cancer detection. However, artefacts, an unclear boundary, poor contrast, and different lesion sizes make detection difficult. To address the issues in skin lesion diagnosis, this study creates the UDLS-DDOA model, an intelligent Unsupervised Deep Learning-based Stacked Auto-encoder (UDLS) optimized by Dynamic Differential Annealed Optimization (DDOA). Pre-processing, segregation,… More >

A. Soujanya^1,*, N. Nandhagopal²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 675-687, 2023, DOI:10.32604/iasc.2023.025930

Abstract Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin lesion segmentation and classification. The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower… More >

C. S. S. Anupama¹, L. Natrayan², E. Laxmi Lydia³, Abdul Rahaman Wahab Sait⁴, José Escorcia-Gutierrez⁵, Margarita Gamarra^6,*, Romany F. Mansour⁷

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1297-1313, 2022, DOI:10.32604/cmc.2022.018396

Abstract Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application… More >

G. Reshma^1,*, Chiai Al-Atroshi², Vinay Kumar Nassa³, B.T. Geetha⁴, Gurram Sunitha⁵, Mohammad Gouse Galety⁶, S. Neelakandan⁷

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 621-634, 2022, DOI:10.32604/iasc.2022.019117

Abstract In recent years, intelligent automation in the healthcare sector becomes more familiar due to the integration of artificial intelligence (AI) techniques. Intelligent healthcare systems assist in making better decisions, which further enable the patient to provide improved medical services. At the same time, skin lesion is a deadly disease that affects people of all age groups. Skin lesion segmentation and classification play a vital part in the earlier and precise skin cancer diagnosis by intelligent systems. However, the automated diagnosis of skin lesions in dermoscopic images is challenging because of the problems such as artifacts (hair, gel bubble, ruler marker),… More >