Inicio  /  Algorithms  /  Vol: 15 Par: 12 (2022)  /  Artículo
ARTÍCULO
TITULO

Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning

Damilola A. Okuboyejo and Oludayo O. Olugbara    

Resumen

The conventional dermatology practice of performing noninvasive screening tests to detect skin diseases is a source of escapable diagnostic inaccuracies. Literature suggests that automated diagnosis is essential for improving diagnostic accuracies in medical fields such as dermatology, mammography, and colonography. Classification is an essential component of an assisted automation process that is rapidly gaining attention in the discipline of artificial intelligence for successful diagnosis, treatment, and recovery of patients. However, classifying skin lesions into multiple classes is challenging for most machine learning algorithms, especially for extremely imbalanced training datasets. This study proposes a novel ensemble deep learning algorithm based on the residual network with the next dimension and the dual path network with confidence preservation to improve the classification performance of skin lesions. The distributed computing paradigm was applied in the proposed algorithm to speed up the inference process by a factor of 0.25 for a faster classification of skin lesions. The algorithm was experimentally compared with 16 deep learning and 12 ensemble deep learning algorithms to establish its discriminating prowess. The experimental comparison was based on dermoscopic images congregated from the publicly available international skin imaging collaboration databases. We propitiously recorded up to 82.52% average sensitivity, 99.00% average specificity, 98.54% average balanced accuracy, and 92.84% multiclass accuracy without prior segmentation of skin lesions to outstrip numerous state-of-the-art deep learning algorithms investigated.

 Artículos similares

       
 
Bojan Ilijoski, Katarina Trojachanec Dineva, Biljana Tojtovska Ribarski, Petar Petrov, Teodora Mladenovska, Milena Trajanoska, Ivana Gjorshoska and Petre Lameski    
A bite from a bug may expose the affected person to serious, life-threatening conditions, which may require immediate medical attention. The identification of the bug bite may be challenging even for experienced medical personnel due to the different man... ver más
Revista: Applied Sciences

 
Muhammad Asad Arshed, Shahzad Mumtaz, Muhammad Ibrahim, Saeed Ahmed, Muhammad Tahir and Muhammad Shafi    
Skin cancer, particularly melanoma, has been recognized as one of the most lethal forms of cancer. Detecting and diagnosing skin lesions accurately can be challenging due to the striking similarities between the various types of skin lesions, such as mel... ver más
Revista: Information

 
Ahmed Eid and Friedhelm Schwenker    
Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by mea... ver más
Revista: Algorithms

 
Flavia Grignaffini, Francesco Barbuto, Lorenzo Piazzo, Maurizio Troiano, Patrizio Simeoni, Fabio Mangini, Giovanni Pellacani, Carmen Cantisani and Fabrizio Frezza    
Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate ... ver más
Revista: Algorithms

 
Saumya Salian, Sudhir Sawarkar     Pág. 59 - 72
The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient?s survival. Building an automated and effective melanoma classification system is the need of the ho... ver más