Performance Evaluation of Attention Mechanism and Spiking Neural Networks on sMRI Data for Suicide Ideation Assessment

The coronavirus disease 2019 (COVID-19) pandemic has had a substantial detrimental impact on mental health, especially depression, and this has led to a high incidence of suicidal ideation (SI) around the globe, with the pandemic's post-peak period seeing the highest incidence in young adults. This study aims to propose an effective non-intrusive method for early detection of SI in young adults utilizing depression as a biomarker in structural magnetic resonance imaging. This paper introduces a hybrid machine learning approach utilizing attention mechanisms and spiking neural networks to differentiate between depression patients without SI and healthy controls. The hybrid method successfully completed the classification task after stratified 5-fold cross-validation, achieving test accuracy, sensitivity, specificity, and area under curve of 94%, 100%, 92%, and 0.96, respectively. The proposed algorithms offer an objective tool for identifying early SI risk in depressed patients without suicidal thoughts, alongside clinical assessment

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.En esta investigación se propone un diseño de arquitectura para el reconocimiento de melanoma (un tipo de cáncer de piel) mediante el uso de una CNN (Red Neuronal Convolucional), trabajo que será de utilidad para investigadores en futuros proyectos en áreas como biomedicina, aprendizaje automático, y otras relacionadas avanzando en sus estudios y mejorando esta propuesta.La CNN se utiliza principalmente en visión por computador (una rama de la inteligencia artificial), aplicada al reconocimiento de patrones en lunares de la piel y para determinar la existencia de melanoma maligno, o no, con un conjunto de datos limitado. El clasificador CNN diseñado y entrenado en este caso se construyó mediante un par de capas de convolución y acumulación para formar una red neuronal de seis capas seguida de la totalmente conectada para completar la arquitectura con un clasificador de salida. La base de datos propuesta para entrenar nuestra CNN es la mayor colección pública de imágenes dermatoscópicas de melanomas y otras lesiones cutáneas, proporcionada por la International Skin Imaging Collaboration (ISIC), patrocinada por la International Society for Digital Imaging of the Skin (ISDIS), un esfuerzo internacional para mejorar el diagnóstico del melanoma. El propósito de esta investigación fue diseñar una Red Neuronal Convolucional con un alto nivel de precisión para ayudar a los profesionales de la medicina con un diagnóstico de melanoma, en este caso, fue posible obtener una precisión hasta del 88,75 %