EfficientNet with Hybrid Attention Mechanisms for Enhanced Breast Histopathology Classification: A Comprehensive Approach

• URL: http://arxiv.org/abs/2410.22392v2
• Date: Mon, 04 Nov 2024 09:56:16 GMT
• Title: EfficientNet with Hybrid Attention Mechanisms for Enhanced Breast Histopathology Classification: A Comprehensive Approach
• Authors: Naren Sengodan,
• Abstract summary: This paper introduces a novel approach integrating Hybrid EfficientNet models with advanced attention mechanisms to enhance feature extraction and focus on critical image regions. We evaluate the performance of our models across multiple magnification scales using publicly available hispathology datasets. The results are validated using metrics such as accuracy, F1-score, precision, and recall, demonstrating the clinical potential of our model in improving diagnostic accuracy.
• Score: 0.0
• License:
• Abstract: Breast cancer histopathology image classification is crucial for early cancer detection, offering the potential to reduce mortality rates through timely diagnosis. This paper introduces a novel approach integrating Hybrid EfficientNet models with advanced attention mechanisms, including Convolutional Block Attention Module (CBAM), Self-Attention, and Deformable Attention, to enhance feature extraction and focus on critical image regions. We evaluate the performance of our models across multiple magnification scales using publicly available histopathological datasets. Our method achieves significant improvements, with accuracy reaching 98.42% at 400X magnification, surpassing several state-of-the-art models, including VGG and ResNet architectures. The results are validated using metrics such as accuracy, F1-score, precision, and recall, demonstrating the clinical potential of our model in improving diagnostic accuracy. Furthermore, the proposed method shows increased computational efficiency, making it suitable for integration into real-time diagnostic workflows.

Related papers

• Lung Disease Detection with Vision Transformers: A Comparative Study of Machine Learning Methods [0.0]
  This study explores the application of Vision Transformers (ViT), a state-of-the-art architecture in machine learning, to chest X-ray analysis. I present a comparative analysis of two ViT-based approaches: one utilizing full chest X-ray images and another focusing on segmented lung regions.
  arXiv  Detail & Related papers  (2024-11-18T08:40:25Z)
• Diagnosis of Malignant Lymphoma Cancer Using Hybrid Optimized Techniques Based on Dense Neural Networks [0.0]
  This study presents a novel hybrid deep learning framework that combines DenseNet201 for feature extraction with a Neural Network (DNN) for classification. Our approach achieved a testing accuracy of 99.33%, demonstrating significant improvements in both accuracy and model interpretability.
  arXiv  Detail & Related papers  (2024-10-09T15:12:35Z)
• Comparative Analysis of Transfer Learning Models for Breast Cancer Classification [10.677937909900486]
  This study investigates the efficiency of deep learning models in distinguishing between Invasive Ductal Carcinoma (IDC) and non-IDC in histopathology slides. We conducted a comparison examination of eight sophisticated models: ResNet-50, DenseNet-121, ResNeXt-50, Vision Transformer (ViT), GoogLeNet (Inception v3), EfficientNet, MobileNet, and SqueezeNet.
  arXiv  Detail & Related papers  (2024-08-29T18:49:32Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example [40.3927727959038]
  This paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images. It enables the rapid and automatic classification of pathological images into benign and malignant groups. It demonstrates that the proposed method effectively enhances the accuracy in classifying pathological images of breast cancer.
  arXiv  Detail & Related papers  (2024-04-12T07:08:05Z)
• Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging [1.8416014644193066]
  We present a novel deep learning framework that leverages the diagnostic value of 3D Optical Coherence Tomography ( OCT) imaging for automated glaucoma detection. We integrate a pre-trained Vision Transformer on retinal data for rich slice-wise feature extraction and a bidirectional Gated Recurrent Unit for capturing inter-slice spatial dependencies. Experimental results on a large dataset demonstrate the superior performance of the proposed method over state-of-the-art ones.
  arXiv  Detail & Related papers  (2024-03-08T22:25:15Z)
• Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration [59.6021678234829]
  We propose a novel method to restore the intermediate features for two sparsely sampled and adjacent video frames. With the integration of our method, the efficiency of three commonly used baselines has been improved by over 50%, with a mere 0.5% reduction in recognition accuracy.
  arXiv  Detail & Related papers  (2023-07-27T13:52:42Z)
• Accurate Detection of Paroxysmal Atrial Fibrillation with Certified-GAN and Neural Architecture Search [1.1744028458220426]
  This paper presents a novel machine learning framework for detecting Paroxysmal Atrial Fibrillation (PxAF) The framework involves a Generative Adversarial Network (GAN) along with a Neural Architecture Search (NAS) Experimental results show that the accuracy of the proposed framework exhibits a high value of 99%.
  arXiv  Detail & Related papers  (2023-01-17T14:04:17Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.