Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)

The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance

Revisión sistemática de la aplicación de Redes Neuronales Artificiales, utilizando Deep Learning, en la detección temprana del cáncer de piel

Las redes neuronales artificiales (RNA’s) basadas en Deep Learning han tomado mayor relevancia en el área de la medicina, ayudando a los especialistas a detectar, en una fase inicial, enfermedades mortales como el cáncer de piel. Por consiguiente, este documento presenta el análisis de las diversas investigaciones que abordan la aplicación de las RNA´s en la detección temprana del cáncer de piel basándose en la metodología propuesta por Kitchenham para el desarrollo óptimo de un artículo de revisión. En base a este análisis, se pudo establecer que Alemania es el país con mayor número de investigaciones basadas en el tema del presente artículo. Además, se encontró que el modelo de arquitectura de RNA, ResNet, es el más abordado en las investigaciones seleccionadas durante el proceso de búsqueda. Por último, se concluyó que un gran porcentaje de investigaciones se desarrollaron en el continente europeo y asiático, dejando un preocupante porcentaje para el sector latinoamericano, también, se pudo corroborar que las RNA´s poseen una gran versatilidad por lo cual pueden ser aplicadas en cualquier rama de la medicina y de la ciencia

Exploring the Potential of Convolutional Neural Networks in Healthcare Engineering for Skin Disease Identification

Skin disorders affect millions of individuals worldwide, underscoring the urgency of swift and accurate detection for optimal treatment outcomes. Convolutional Neural Networks (CNNs) have emerged as valuable assets for automating the identification of skin ailments. This paper conducts an exhaustive examination of the latest advancements in CNN-driven skin condition detection. Within dermatological applications, CNNs proficiently analyze intricate visual motifs and extricate distinctive features from skin imaging datasets. By undergoing training on extensive data repositories, CNNs proficiently classify an array of skin maladies such as melanoma, psoriasis, eczema, and acne. The paper spotlights pivotal progressions in CNN-centered skin ailment diagnosis, encompassing diverse CNN architectures, refinement methodologies, and data augmentation tactics. Moreover, the integration of transfer learning and ensemble approaches has further amplified the efficacy of CNN models. Despite their substantial potential, there exist pertinent challenges. The comprehensive portrayal of skin afflictions and the mitigation of biases mandate access to extensive and varied data pools. The quest for comprehending the decision-making processes propelling CNN models remains an ongoing endeavor. Ethical quandaries like algorithmic predisposition and data privacy also warrant significant consideration. By meticulously scrutinizing the evolutions, obstacles, and potential of CNN-oriented skin disorder diagnosis, this critique provides invaluable insights to researchers and medical professionals. It underscores the importance of precise and efficacious diagnostic instruments in ameliorating patient outcomes and curbing healthcare expenditures