Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification DOI Creative Commons
Marcos Gabriel Mendes Lauande, Geraldo Bráz, João Dallyson Sousa de Almeida

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10536 - 10536

Published: Nov. 15, 2024

Histopathological analysis is an essential exam for detecting various types of cancer. The process traditionally time-consuming and laborious. Taking advantage deep learning models, assisting the pathologist in diagnosis possible. In this work, a study was carried out based on DenseNet neural network. It consisted changing its architecture through combinations Transformer MBConv blocks to investigate impact classifying histopathological images penile Due limited number samples dataset, pre-training performed another larger lung colon cancer image dataset. Various these architectural components were systematically evaluated compare their performance. results indicate significant improvements feature representation, demonstrating effectiveness combined elements resulting F1-Score up 95.78%. Its diagnostic performance confirms importance techniques men’s health.

Language: Английский

Enhancing lung cancer diagnostic accuracy and reliability with LCDViT: an expressly developed vision transformer model featuring explainable AI DOI
Amol Satsangi, K. Srinivas, A. Charan Kumari

et al.

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

Language: Английский

Citations

0
Automatic lung cancer detection from CT image using optimized Robust Deformed Convolutional Neural Network with TriHorn-Net DOI

Chakradhar Reddy,

M. V. D. Prasad

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127124 - 127124

Published: March 1, 2025

Language: Английский

Citations

0
Optimizing lung cancer classification using transformer and Gooseneck Barnacle Optimization DOI

Arockia Jesuraj Yagappan,

Hemalatha Karuppiah,

Mohanapriya Muthusamy

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127413 - 127413

Published: April 1, 2025

Language: Английский

Citations

0
A Wavelet-Based Two-Stage Vision Transformer Model for Histological Subtypes Classification of Lung Cancers on CT Images DOI Open Access
Eri Matsuyama,

Haruyuki Watanabe,

Noriyuki Takahashi

et al.

Open Journal of Medical Imaging, Journal Year: 2025, Volume and Issue: 15(02), P. 57 - 72

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Combined Approach of Vision Transformer and Transfer Learning Based Model for Accurate Lung Cancer Classification DOI
Arvind Kumar,

Muhammad Rashid Ansari,

Koushlendra Kumar Singh

et al.

SN Computer Science, Journal Year: 2025, Volume and Issue: 6(5)

Published: May 30, 2025

Language: Английский

Citations

0
GSC-DVIT: A vision transformer based deep learning model for lung cancer classification in CT images DOI

Durgaprasad Mannepalli,

K. T. Tan, Sivaneasan Bala Krishnan

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 103, P. 107371 - 107371

Published: Dec. 30, 2024

Language: Английский

Citations

1
Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification DOI Creative Commons
Marcos Gabriel Mendes Lauande, Geraldo Bráz, João Dallyson Sousa de Almeida

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10536 - 10536

Published: Nov. 15, 2024

Histopathological analysis is an essential exam for detecting various types of cancer. The process traditionally time-consuming and laborious. Taking advantage deep learning models, assisting the pathologist in diagnosis possible. In this work, a study was carried out based on DenseNet neural network. It consisted changing its architecture through combinations Transformer MBConv blocks to investigate impact classifying histopathological images penile Due limited number samples dataset, pre-training performed another larger lung colon cancer image dataset. Various these architectural components were systematically evaluated compare their performance. results indicate significant improvements feature representation, demonstrating effectiveness combined elements resulting F1-Score up 95.78%. Its diagnostic performance confirms importance techniques men’s health.

Language: Английский

Citations

0