Transformer-Based Neural Architectures ForAutomatedCancer Classification In Histopathology Images
Year: 2024
Type: article
Abstract: Timely identification of metastatic cancer via accurate image classification is essential for enhancing patient outcomes.This research introduces a deep learning method for automated tumor identification through Transformer-Based Neural Architectures applied to histopathological images.Our model underwent training using a dataset composed of 96x96 pixel microscopic images and demonstrated remarkable performance, attaining a training accuracy of 93.9% and a validation accuracy of 93.1%.The model showed excellent effectiveness in differentiating "no tumor tissue" from "tumor tissue," reaching an ROC-AUC score of 0.9799.These findings indicate that our method is very proficient at correctly identifying tumor areas, paving the path for better diagnostic instruments in medical image analysis.
Author: Lalitha Bhavani Konkyana
Institution: Aditya Institute of Technology and Management
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405416287