Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network (1811.04241v1)

Abstract: The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer Classification Challenge 2015. The experimental results are compared against the existing machine learning and deep learning-based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, Area Under the Curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.

• The paper introduces an innovative IRRCNN model that merges Inception, residual, and recurrent networks to improve feature extraction.
• It demonstrates superior classification accuracy by leveraging diverse datasets and robust data augmentation techniques.
• Results indicate up to 97% accuracy in multi-class tasks, highlighting the model’s potential for clinical diagnostics.

Breast Cancer Classification from Histopathological Images Using an IRRCNN Model

The paper "Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network" explores an advanced deep learning approach for the automated classification of breast cancer. This research focuses on using a newly proposed Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model, which amalgamates the Inception Network, Residual Network (ResNet), and Recurrent Convolutional Neural Network (RCNN) to enhance performance.

Main Contributions and Methodology

1. Innovative Model Architecture: The IRRCNN model integrates multiple advanced neural architectures. By combining the fundamental components of Inception, residual layers, and recurrent connections, this approach harnesses the benefits of each architecture, such as improved feature extraction and efficient parameter usage.
2. Dataset Utilization: The paper applies this IRRCNN model to two publicly available datasets—BreakHis and the Breast Cancer Classification Challenge 2015. These datasets provide diverse histopathological images at various magnification factors (40×, 100×, 200×, and 400×), presenting challenges in image interpretation and classification tasks.
3. Experimental Evaluation: The authors conducted experiments with image-based and patch-based methods, considering both the binary classification of benign versus malignant tumors and multi-class classification across different cancer subtypes.
4. Performance Metrics: The evaluation utilized several performance indicators, including sensitivity, Area Under the Curve (AUC), the Receiver Operating Characteristic (ROC) curve, and global accuracy. Performance comparisons show that the IRRCNN consistently outperforms existing machine learning and deep learning models.

Numerical Results and Findings

The results demonstrate the IRRCNN model's superiority across several classification scenarios. For multi-class classification with the BreakHis dataset, the IRRCNN achieved testing accuracies of 97.09% (40×), 97.57% (100×), 97.29% (200×), and 97.22% (400×) when using data augmentation. These results indicate a significant accuracy improvement compared to prior methodologies, highlighting the effectiveness of the IRRCNN architecture in handling heterogeneity and variability in pathological image data.

Furthermore, image-wise classification on the Breast Cancer Classification Challenge 2015 dataset reached up to 100% accuracy using a winner-take-all approach with random patch selection, marking substantial progress in overcoming the common challenges in medical image classification.

Implications and Future Directions

The implications of this research are notable for clinical practice, offering a potential tool for aiding pathologists in reliable breast cancer diagnosis. The IRRCNN model, with its demonstrated high accuracy, could be integrated into clinical diagnostic systems to provide prompt and precise tumor classification, thereby enhancing the efficiency and reliability of diagnoses.

Future developments could focus on addressing data annotation challenges, such as limited labeled datasets, which are a significant barrier in medical imaging. Additionally, further research could investigate the adaptability of the IRRCNN model to other types of medical image analysis and different cancer types, potentially broadening its impact in the field of automated diagnostics.

Overall, this work positions the IRRCNN model as a compelling contribution to the domain of histopathological image analysis, offering a promising avenue for advancing machine learning applications in healthcare.