Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images

Jawad, M. Abdul; Khursheed, Farida

doi:10.1016/j.bspc.2022.103935

Cited by 9 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The author suggested in [11] In order to automatically extract features from the images for both binary classification and self-learning classification tasks, ten pre-trained deep CNNs were used. Several methods were used in the process, such as transfer learning and data augmentation for the training dataset.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging Deep Learning for Breast Cancer Detection : An Comprehensive study

Theja,

2024

Preprint

View full text Add to dashboard Cite

Breast cancer is a significant health concern globally, emphasizing the need for accurate diagnostic tools for early detection and treatment. In this research, we propose a novel methodology leveraging deep learning techniques for classifying breast histopathology images as benign or malignant. We employ state of the art convolutional neural networks (CNNs), including VGG16, VGG19, ResNet50, ResNet101, and a custom CNN architecture, to extract discriminative features from histopathology images. One of the key contributions of this work is the incorporation of class weights into the training process, aiming to address class imbalance in the da-taset and enhance model performance. We evaluate the efficacy of our approach using various performance metrics, including accuracy, precision, recall, and F1-score, on the publicly available BreaKHis dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

“…In [13] integrating preprocessing techniques, hybrid CLSTM models, and supplementary classifiers like SVM, the proposed method aims to optimize performance in tasks such as breast cancer diagnosis, where accurate classification of sequential data is crucial for effective decision-making and patient care.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Deep Learning for Breast Cancer Detection : An Comprehensive study

Theja,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In binary breast cancer classification [53][54], training of the modified GoogLeNet architecture which has the attention mechanism is done on the BreaKHis dataset that contains annotated histopathological images classified as benign and malignant. The model's parameters are optimized for this purpose during training to ensure accurate classifications of the images as either benign or malignant.…”

Section: F Binary Classification For Breakhismentioning

confidence: 99%

Enhancing Breast Cancer Classification using a Modified GoogLeNet Architecture with Attention Mechanism

2024

IJSER

View full text Add to dashboard Cite

Breast cancer incidence has been soaring sharply, and this is causing grave concern worldwide due to its high mortality rates. It should be correctly diagnosed in the early stages in order to achieve better patient outcomes. Over the last decade, there has been a great demand for diagnosis systems based on AI that could be used in breast cancer detection and classification. These computerized devices utilize deep learning algorithms to analyze medical scans thereby allowing for subtle abnormality recognition and distinguishing malignant from benign tumors. Computer-aided diagnosis named CAD systems can assist radiologists and pathologists to be more precise with their diagnoses while at the same time increasing productivity. Furthermore, recent advances in CNN architectures coupled with attention mechanisms have further improved CAD systems for breast cancer diagnosis. Attention-based CNN models focus on crucial regions hence enhancing classification accuracy and reliability. In this study, we introduce a new approach that improves the classification of breast cancer using GoogLeNet architecture modified by an attention mechanism that is based on image regions. The modified GoogLeNet has a spatial transformer network (STN), which allows it to focus on significant areas in breast histopathology images using selective attention. Through the attention mechanism, the model becomes better at learning discriminatory features that indicate different subtypes of breast cancer. In order to evaluate the effectiveness of this method, we implemented experiments using the BreaKHis dataset for classifying breast carcinomas. This dataset has been intentionally collected under various magnifications so as to facilitate binary and multiple classification tasks. These outcomes clearly show that modified GoogLeNet with attention outperforms the original GoogLeNet architecture in terms of accuracy. For binary classification, the proposed model demonstrated an accuracy rate of 98.08%, whereas GoogLeNet's rate was 94.99%. For multi-class classification, at 100x magnification, this model achieved an accuracy of 94.63% while the accuracy of the original GoogLeNet was 85.06%. It is evident from these findings that the efficiency of breast cancer diagnosis significantly improved under this proposed approach. The findings of the study show that incorporating the modified GoogLeNet framework with an attention mechanism in CAD systems for breast cancer detection can improve their performance. Combining deep learning and attention models together can lead to more accurate treatment decisions and better patient results. More efforts are needed to further develop CAD systems in this area to assist ongoing endeavors towards upgrading them and ultimately contribute to saving many lives in the fight against breast cancer.

show abstract

Classifying breast cancer using transfer learning models based on histopathological images

Rana

Bhushan

2023

Neural Comput & Applic

View full text Add to dashboard Cite

Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images

Cited by 9 publications

References 30 publications

Leveraging Deep Learning for Breast Cancer Detection : An Comprehensive study

Leveraging Deep Learning for Breast Cancer Detection : An Comprehensive study

Enhancing Breast Cancer Classification using a Modified GoogLeNet Architecture with Attention Mechanism

Classifying breast cancer using transfer learning models based on histopathological images

Contact Info

Product

Resources

About