High-Performance Classification of Breast Cancer Histopathological Images Using Fine-Tuned Vision Transformers on the BreakHis Dataset DOI Creative Commons

Venkat Gella

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

Abstract Breast cancer remains one of the most prevalent and deadly cancers worldwide, making accurate diagnosis critical for effective treatment. Histopathological image classification is a key task in medical diagnostics detection. This paper presents state-of-the-art performance histopathological breast using novel approach with Vision Transformer (ViT) model fine-tuned BreakHis dataset. The dataset, comprising 7,909 images across various magnification levels, serves as crucial benchmark evaluating machine learning models this domain. While previous works have explored use ViTs task, our fine-tunes ViT pre-trained on ImageNet Ranger optimizer, achieving unprecedented performance. experimental results show that achieves an accuracy 99.99%, precision 99.98%, recall F1 score specificity 100.00%, false discovery rate (FDR) 0.00%, negative (FNR) 0.02%, positive (FPR) Matthews correlation coefficient (MCC) 99.97%, predictive value (NPV) 99.96%. represents highest ever achieved binary any model, underscoring potential Transformers to substantially enhance diagnostic analysis improve clinical outcomes. Transfer was also performed BACH dataset invasive ductal carcinomas (IDC).

Language: Английский

Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis DOI
Chukwuebuka Joseph Ejiyi, Dongsheng Cai,

Delali Linda Fiasam

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 187, P. 109791 - 109791

Published: Feb. 11, 2025

Language: Английский

Citations

0
A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis DOI

Shumaila Batool,

Saima Zainab

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 183, P. 109215 - 109215

Published: Oct. 4, 2024

Language: Английский

Citations

2
High-Performance Classification of Breast Cancer Histopathological Images Using Fine-Tuned Vision Transformers on the BreakHis Dataset DOI Creative Commons

Venkat Gella

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

Abstract Breast cancer remains one of the most prevalent and deadly cancers worldwide, making accurate diagnosis critical for effective treatment. Histopathological image classification is a key task in medical diagnostics detection. This paper presents state-of-the-art performance histopathological breast using novel approach with Vision Transformer (ViT) model fine-tuned BreakHis dataset. The dataset, comprising 7,909 images across various magnification levels, serves as crucial benchmark evaluating machine learning models this domain. While previous works have explored use ViTs task, our fine-tunes ViT pre-trained on ImageNet Ranger optimizer, achieving unprecedented performance. experimental results show that achieves an accuracy 99.99%, precision 99.98%, recall F1 score specificity 100.00%, false discovery rate (FDR) 0.00%, negative (FNR) 0.02%, positive (FPR) Matthews correlation coefficient (MCC) 99.97%, predictive value (NPV) 99.96%. represents highest ever achieved binary any model, underscoring potential Transformers to substantially enhance diagnostic analysis improve clinical outcomes. Transfer was also performed BACH dataset invasive ductal carcinomas (IDC).

Language: Английский

Citations

0