Quantifying the effects of segmentation in image classification for melanoma recognition - Araújo, Rafael Luz; Luz, Daniel de S.; Lima, Bruno Vicente de; Marques, Júlio V. M.; Veras, Rodrigo de M. S.; C. Filho, Antônio O. de; Araújo, Flávio H. D.; Veloso e Silva, Romuere Rodrigues (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2024: Anais do XXI Encontro Nacional de Inteligência Artificial e ComputacionalDO - 10.5753/eniac.2024.245228, 2024)
Melanoma remains the leading cause of skin cancer-related deaths worldwide, emphasizing the critical need for early detection to enhance survival rates. Computational methods are pivotal in aiding its diagnosis through medical imaging, necessitating accurate lesion segmentation to facilitate effective interpretation. Our study investigates the comparative efficacy of skin lesion classification with and without segmentation, leveraging pre-trained convolutional neural networks (CNNs) and CapsNet architectures. Findings underscore CNNs’ superiority, highlighting segmentation’s beneficial impact on their classification performance, while CapsNet exhibits a degree of independence from segmentation.