Breast Cancer Histopathology Images Research Articles

Breast cancer histopathology image classification using an ensemble of optimized pretrained models with a trainable ensemble strategy classifier

Breast cancer histopathology image classification using an ensemble of optimized pretrained models with a trainable ensemble strategy classifier

Vision transformer based convolutional neural network for breast cancer histopathological images classification

Vision transformer based convolutional neural network for breast cancer histopathological images classification

Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification.

Breast cancer diagnosis from histopathology images is often time consuming and prone to human error, impacting treatment and prognosis. Deep learning diagnostic methods offer the potential for improved accuracy and efficiency in breast cancer detection and classification. However, they struggle with limited data and subtle variations within and between cancer types. Attention mechanisms provide feature refinement capabilities that have shown promise in overcoming such challenges. To this end, this paper proposes the Efficient Channel Spatial Attention Network (ECSAnet), an architecture built on EfficientNetV2 and augmented with a convolutional block attention module (CBAM) and additional fully connected layers. ECSAnet was fine-tuned using the BreakHis dataset, employing Reinhard stain normalization and image augmentation techniques to minimize overfitting and enhance generalizability. In testing, ECSAnet outperformed AlexNet, DenseNet121, EfficientNetV2-S, InceptionNetV3, ResNet50, and VGG16 in most settings, achieving accuracies of 94.2% at 40×, 92.96% at 100×, 88.41% at 200×, and 89.42% at 400× magnifications. The results highlight the effectiveness of CBAM in improving classification accuracy and the importance of stain normalization for generalizability.

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology.

Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.

FCCS-Net: Breast cancer classification using Multi-Level fully Convolutional-Channel and spatial attention-based transfer learning approach

Breast cancer ranks among one of the most lethal cancer varieties among the many types that exist. Timely detection is paramount, as late diagnosis can exacerbate its severity. Computer-aided detection systems can complement the clinician in early decision-making. Therefore, in this study, a multi-level, complete convolution-driven attention-based transfer learning approach named ‘FCCS-Net’ has been proposed, for breast cancer classification. In contrast to the shared multi-layer perceptron (MLP)-based attention mechanism, the proposed approach employs a fully convolutional attention mechanism to focus the important cellular features in inter-channel and intra-channel feature space. This proposed attention is applied across multiple levels of a pre-trained ResNet18 model, supplemented with additional residual connections. The performance of the proposed FCCS-Net is tested on publicly available datasets such as ‘BreakHis’,‘IDC’ and ‘BACH’, containing breast cancer histopathology images. On the BreakHis dataset, the proposed method achieves accuracy rates of 99.25%, 98.32%, 99.50%, and 96.98% at 40X, 100X, 200X, and 400X optical zoom levels, respectively. In the case of the IDC dataset, a classification accuracy of 90.58% is attained at 40X magnifications, whereas with BACH dataset 91.25% average classification accuracy has been obtained. These findings establish the robustness and efficacy of the FCCS-Net in detecting breast cancer through histopathology images. The area focused by each attention layer has also been visually explained. The integration of multi-level, fully convolutional attention with supplementary residual connections holds the potential to advance breast cancer detection methodologies. The relevant PyTorch code for implementing the FCCS-Net model can be accessed at https://github.com/maurya123ritesh47/FCCS-Net.

Adaptive Threshold Learning in Frequency Domain for Classification of Breast Cancer Histopathological Images

Breast cancer has become the most common cancer in the world, and biopsy is the most reliable and widely used technique for detecting breast cancer. However, observation of histopathological images is time-consuming and labor-intensive. Currently, CNN has become the mainstream method for breast cancer histopathological image classification research. However, some studies have found that the optical microscope-generated histopathological images have noise, and the output of a well-trained convolutional neural network in image classification tasks can change drastically due to small variations in the input. Therefore, the quality of the image significantly affects the accuracy of the classification. Wavelet transform is a commonly used denoising method, but the selection of the threshold is a difficult problem, and traditional methods are difficult to find the appropriate threshold quickly and accurately. This paper proposes an adaptive threshold selection method that combines threshold selection steps with deep learning methods by using the threshold as a parameter in the CNN model to train. In this way, we associate the threshold with the classification result of the model and find the appropriate value for that image and task by back-propagation in training. The method was experimented on publicly available datasets BreaKHis and BACH. The results in BreaKHis (40x: 94.37%, 100x: 93.85%, 200x: 91.63%, 400x: 93.31%), and BACH (91.25%) demonstrate that our adaptive threshold selection method can improve classification accuracy and is significantly superior to traditional threshold selection methods.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification. This study aims to design a model to classify BC Histopathology images accurately by leveraging segmentation techniques. This work proposes a combined segmentation and classification approach for classifying BC using histopathology images to address these issues. Chan-Vese algorithm is used for segmentation to accurately delineate regions of interest within the histopathology images, followed by the proposed SegEIR-Net (Segmentation using EfficientNet, InceptionNet, and ResNet) for classification. Bilateral Filtering is also employed for noise reduction. The proposed model uses three significant networks, ResNet, InceptionNet, and EfficientNet, concatenates the outputs from each block followed by Dense and Dropout layers. The model is trained on the breakHis dataset for four different magnifications and tested on BACH (BreAst Cancer Histology) and UCSB (University of California, Santa Barbara) datasets. SegEIR-Net performs better than the existing State-of-the-Art (SOTA) methods in terms of accuracy on all three datasets, proving the robustness of the proposed model. The accuracy achieved on breakHis dataset are 98.66%, 98.39%, 97.52%, 95.22% on different magnifications, and 93.33% and 96.55% on BACH and UCSB datasets. These performance results indicate the robustness of the proposed SegEIR-Net framework in accurately classifying BC from histopathology images.

Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT

Developing a clinical AI model necessitates a significant amount of highly curated and carefully annotated dataset by multiple medical experts, which results in increased development time and costs. Self-supervised learning (SSL) is a method that enables AI models to leverage unlabelled data to acquire domain-specific background knowledge that can enhance their performance on various downstream tasks. In this work, we introduce CypherViT, a cluster-based histo-pathology phenotype representation learning by self-supervised multi-class-token hierarchical Vision Transformer (ViT). CypherViT is a novel backbone that can be integrated into a SSL pipeline, accommodating both coarse and fine-grained feature learning for histopathological images via a hierarchical feature agglomerative attention module with multiple classification (cls) tokens in ViT. Our qualitative analysis showcases that our approach successfully learns semantically meaningful regions of interest that align with morphological phenotypes. To validate the model, we utilize the DINO self-supervised learning (SSL) framework to train CypherViT on a substantial dataset of unlabeled breast cancer histopathological images. This trained model proves to be a generalizable and robust feature extractor for colorectal cancer images. Notably, our model demonstrates promising performance in patch-level tissue phenotyping tasks across four public datasets. The results from our quantitative experiments highlight significant advantages over existing state-of-the-art SSL models and traditional transfer learning methods, such as those relying on ImageNet pre-training.

Multi-Branch Spectral Channel Attention Network for Breast Cancer Histopathology Image Classification

Deep-learning-based breast cancer image diagnosis is currently a prominent and growingly popular area of research. Existing convolutional-neural-network-related methods mainly capture breast cancer image features based on spatial domain characteristics for classification. However, according to digital signal processing theory, texture images usually contain repeated patterns and structures, which appear as intense energy at specific frequencies in the frequency domain. Motivated by this, we make an attempt to explore a breast cancer histopathology classification application in the frequency domain and further propose a novel multi-branch spectral channel attention network, i.e., the MbsCANet. It expands the interaction of frequency domain attention mechanisms from a multi-branch perspective via combining the lowest frequency features with selected high frequency information from two-dimensional discrete cosine transform, thus preventing the loss of phase information and gaining richer context information for classification. We thoroughly evaluate and analyze the MbsCANet on the publicly accessible BreakHis breast cancer histopathology dataset. It respectively achieves the optimal image-level and patient-level classification results of 99.01% and 98.87%, averagely outperforming the spatial-domain-dominated models by a large margin, and visualization results also demonstrate the effectiveness of the MbsCANet for this medical image application.

Ensemble of Deep Features for Breast Cancer Histopathological Image Classification

Abstract Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model’s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.

WHRIME: A weight-based recursive hierarchical RIME optimizer for breast cancer histopathology image segmentation

In medical image processing, multi-threshold image segmentation has been challenging, as selecting appropriate thresholds is crucial for distinguishing different structures within an image, especially when dealing with breast cancer images. Breast cancer images are complex with multiple tissue types, which pose challenges to precise diagnosis. A weight-based recursive hierarchical bootstrapping rime algorithm (WHRIME) is proposed to tackle this case effectively. The proposed WHRIME segregates the population into elite and non-elite individuals. The hierarchical bootstrapping strategy ensures comprehensive exploration of the solution space, with elite individuals guiding the position updates of non-elite individuals. A weighted method is introduced based on solution quality differences to maintain population diversity and enhance convergence accuracy. We apply WHRIME in multi-threshold image segmentation on breast cancer histopathology images. Experimental results on both the IEEE CEC 2017 benchmark suit and breast cancer histopathology images from the Databiox dataset validate the superiority of WHRIME over competing algorithms, affirming WHRIME’s capability in addressing complex breast cancer histopathology image segmentation problems.

Collaborative Transfer Network for Multi-Classification of Breast Cancer Histopathological Images.

The incidence of breast cancer is increasing rapidly around the world. Accurate classification of the breast cancer subtype from hematoxylin and eosin images is the key to improve the precision of treatment. However, the high consistency of disease subtypes and uneven distribution of cancer cells seriously affect the performance of multi-classification methods. Furthermore, it is difficult to apply existing classification methods to multiple datasets. In this article, we propose a collaborative transfer network (CTransNet) for multi-classification of breast cancer histopathological images. CTransNet consists of a transfer learning backbone branch, a residual collaborative branch, and a feature fusion module. The transfer learning branch adopts the pre-trained DenseNet structure to extract image features from ImageNet. The residual branch extracts target features from pathological images in a collaborative manner. The feature fusion strategy of optimizing these two branches is used to train and fine-tune CTransNet. Experiments show that CTransNet achieves 98.29% classification accuracy on the public BreaKHis breast cancer dataset, exceeding the performance of state-of-the-art methods. Visual analysis is carried out under the guidance of oncologists. Based on the training parameters of the BreaKHis dataset, CTransNet achieves superior performance on other two public breast cancer datasets (breast-cancer-grade-ICT and ICIAR2018_BACH_Challenge), indicating that CTransNet has good generalization performance.

LMHistNet: Levenberg–Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images

LMHistNet: Levenberg–Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images

Data Augmentation for Medical Image Classification based on Gaussian Laplacian Pyramid Blending with a Similarity Measure.

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.

FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION ALGORITHMS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoder-decoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including Marine Predators Algorithm, Tunicate Swarm Algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization Algorithm, and Jelly Fish optimization algorithm. Artificial Hummingbird Algorithm based on Manta Ray Foraging Optimization Algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

Breast Cancer Histopathology Images Classification Through Multi-View Augmented Contrastive Learning and Pre-Learning Knowledge Distillation

Breast Cancer Histopathology Images Classification Through Multi-View Augmented Contrastive Learning and Pre-Learning Knowledge Distillation

Classification of Breast Cancer Histopathological Images Using Transfer Learning with DenseNet121

Breast cancer (BC) continues to be a prominent issue in global public health, emphasizing the need for precise and timely detection. This paper employs a deep learning (DL) approach to introduce an extensive methodology for categorizing histopathology images associated with breast cancer into automated binary classifications. The proposed framework architecture is validated on the standard database which is accessible to the public, called Breast Cancer Histopathological Database (BreakHis). Data augmentation techniques are employed for the pre-processing stage. This paper uses the DenseNet 121 pre-trained model for feature extraction and fully connected layers (FCL) to fine-tune the model further. In this experiment, the highest accuracy of 96.09% is observed with the 100X. The experimental results showed an improvement in accuracy for all the magnification factors compared to the existing works.

Enhancing breast cancer classification via histopathological image analysis: Leveraging self-supervised contrastive learning and transfer learning

Breast cancer, a significant threat to women's health, demands early detection. Automating histopathological image analysis offers a promising solution to enhance efficiency and accuracy in diagnosis. This study addresses the challenge of breast cancer histopathological image classification by leveraging the ResNet architecture, known for its depth and skip connections. In this work, two distinct approaches were pursued, each driven by unique motivations. The first approach aimed to improve the learning process through self-supervised contrastive learning. It utilizes a small subset of the training data for initial model training and progressively expands the training set by incorporating confidently labeled data from the unlabeled pool, ultimately achieving a reliable model with limited training data. The second approach focused on optimizing the architecture by combining ResNet50 and Inception module to get a lightweight and efficient classifier. The dataset utilized in this work comprises histopathological images categorized into benign and malignant classes at varying magnification levels (40X, 100X, 200X, 400X), all originating from the same source image. The results demonstrate state-of-the-art performance, achieving 98% accuracy for images magnified at 40X and 200X, and 94% for 100X and 400X. Notably, the proposed architecture boasts a substantially reduced parameter count of approximately 3.6 million, contrasting with existing leading architectures, which possess parameter sizes at least twice as large.

An enhanced multi-scale deep convolutional orchard capsule neural network for multi-modal breast cancer detection

Breast cancer is the second-leading cause of cancer death in women. Breast cells develop into malignant, cancerous lumps, the first signs of breast cancer. Breast cancer can be discovered by the automated diagnostic system when it is still too little to be found by conventional medical methods. Early breast cancers identified with automated screening and diagnosis technologies are generally treatable. This study proposes an enhanced multi-scale deep Convolutional Capsule Neural Network (CapsNet) optimized with Orchard Optimization Algorithm for breast cancer detection. The proposed system consists of preprocessing, feature extraction, segmentation, and classification process. Two input images are taken initially: the Breast Cancer Histopathology Images dataset and the Infrared Thermal Images dataset. The quality of the collected data is improved, and unwanted noises are removed. The features are extracted to segment the image to derive a Region of Interest for effectively segmenting the affected region. Finally, the images are classified as benign/malignant for histopathology images and healthy/cancer for thermal images. The proposed CapsNet is implemented in Python, run for 200 epochs, and compared with existing methods in terms of evaluation metrics. The result shows that the proposed CapsNet attained 99.74 % accuracy, 0.0482 binary entropy loss on the Breast Cancer Histopathology Image dataset and 97 % accuracy, 0.2081 binary entropy loss on the Infrared Thermal Images dataset while incrementing the epochs at each level.

The Techniques Used in Mitosis Detection in Breast Cancer Histopathology Images: A Survey

Breast cancer consider as the second cause of death around the world after heart disease, and it is the primary cause of death for women. Timely detection of breast cancer plays a crucial role in lowering mortality rates, as it enhances the patient's prospects of survival through prompt diagnosis and appropriate treatment. The discovery of the mitotic number is one of the necessary procedures that must be performed for a person suffering from breast cancer because it is an important marker for determining the aggressiveness of the tumor. According to the Nottingham scale, it gives 3 degrees to determine the degree of the tumor, whether it is of the first degree, the second degree, or the third degree of seriousness. Deep learning algorithms have many contributions in the medical fields, including in the field of mitotic number discovery, as the mitotic number process is a difficult and tiring task that requires time and effort from pathologists (diagnostic doctors), because the work environment is under microscopes with high magnification degrees, for this reason deep learning techniques were used to reduce the burden on diagnostic doctors and save time for the patient to know the result of his examination, as the biopsy results in developed countries take from 10 days to two weeks for the results to appear. In this survey, we will evaluate the deep learning techniqus employed for mitotic number detection.