Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification
Source abstract
Abstract
Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Genital Health and Disease · Artificial Intelligence in Healthcare and Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Applied Computing Group (NCA-UFMA), Federal University of Maranhão, São Luís 65080-805, MA, Brazil
Postgraduate Program in Adult Health/PPGSAD, Federal University of Maranhão, São Luís 65080-085, MA, Brazil
School of Science and Technology, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal
ALGORITMI Research Centre, University of Minho, 4800-058 Guimarães, Portugal
Funding
Fundação para a Ciência e Tecnologia, IP (FCT) · Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) · CAPES PDPG Amazônia Legal 0810/2020 · Fundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do Maranhão (FAPEMA) · Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
References (42)
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