Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

Breast cancer remains a major global health concern, particularly among women, where early detection is crucial for effective treatment and increased survival rates. Manual diagnosis through histopathological examination is often time-consuming, prone to human error, and requires expert interpretation. In response to this challenge, we developed a deep learning-based system to automate the detection and classification of breast cancer using microscopic biopsy images, aimed at enhancing diagnostic accuracy and speed in clinical workflows. Our project employs convolutional neural networks (CNNs) to classify histopathological images into three distinct categories: normal, benign, and malignant. We utilized a publicly available dataset containing high-resolution microscopic images and implemented preprocessing techniques to improve image quality, normalize input dimensions, and optimize feature extraction. The model was developed and trained using TensorFlow with GPU support, allowing for efficient computation and faster training cycles. Key Words: Breast cancer, Cancer detection, Cancer classification, Histopathological images, microscopic images, Digital pathology, medical imaging

Breast cancer encompasses various subtypes with distinct prognoses, necessitating accurate stratification methods. Current techniques rely on quantifying gene expression in limited subsets. Given the complexity of breast tissues, effective detection and classification of breast cancer is crucial in medical imaging. This study introduces a novel method, MPa-DCAE, which uses a multi-patch-based deep convolutional auto-encoder (DCAE) framework combined with VGG19 to detect and classify breast cancer in histopathology images. The proposed MPa-DCAE model leverages the hierarchical feature extraction capabilities of VGG19 within a DCAE framework, designed to capture intricate patterns in histopathology images. By using a multi-patch approach, regions of interest are extracted from pathology images to facilitate localized feature learning, enhancing the model's discriminatory power. The auto-encoder component enables unsupervised feature learning, increasing resilience and adaptability to variations in image features. Experiments were conducted at various magnifications on the CBIS-DDSM and MIAS datasets to validate model performance. Experimental results demonstrated that the MPa-DCAE model outperformed existing methods. For the CBIS-DDSM dataset, the model achieved a precision of 97.96%, a recall of 94.85%, and an accuracy of 98.36%. For the MIAS dataset, it achieved a precision of 97.99%, a recall of 97.2%, and an accuracy of 98.95%. These results highlight the model's robustness and potential for clinical application in computer-assisted diagnosis. The MPa-DCAE model, integrating VGG19 and DCAE, proves to be an effective, automated approach for diagnosing breast cancer. Its high accuracy and generalizability make it a promising tool for clinical practice, potentially improving patient care in histopathology-based breast cancer diagnosis.

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.

Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization.

Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.

Breast Cancer (BC) detection and classification are critical tasks in medical diagnostics. The lives of patients can be greatly enhanced by the precise and early detection of BC. This study suggests a novel approach for detecting BC that combines deep learning models and sophisticated image processing techniques to address those shortcomings. The BC dataset was pre-processed using histogram equalization and adaptive filtering. Data augmentation was performed using cycle-consistent GANs (CycleGANs). Handcrafted features like Haralick features, Gabor filters, contour-based features, and morphological features were extracted, along with features from deep learning architecture VGG16. Then, we employed a hybrid optimization model, combining the Sparrow Search Algorithm (SSA) and Red Deer Algorithm (RDA), called Hybrid Red Deer with Sparrow optimization (HRDSO), to select the most informative subset of features. For detecting BC, we proposed a new DenseXtNet architecture by combining DenseNet and optimized ResNeXt, which is optimized using the hybrid optimization model HRDSO. The proposed model was evaluated using various performance metrics and compared with existing methods, demonstrating that its accuracy is 97.58% in BC detection. MATLAB was utilized for implementation and evaluation purposes.

ความเป็นมา การตอบสนองทางพยาธิวิทยาภายหลังการได้รับยาเคมีบำบัดก่อนผ่าตัดมีความสัมพันธ์กับผลลัพธ์ทางคลินิก ปัจจุบันไม่มีปัจจัยใดที่สามารถทำนายการตอบสนองต่อยาเคมีบำบัดในมะเร็งเต้านมชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูได้อย่างแม่นยำ เช่นเดียวกับ Ki-67 การแสดงออกของโปรตีน MCM2 ซึ่งมีหน้าที่ควบคุมการสังเคราะห์ดีเอ็นเอในระดับสูงบ่งชี้ถึงการแบ่งตัวของเซลล์ที่รวดเร็ว และมีความสัมพันธ์กับการพยากรณ์โรคที่ไม่ดีในมะเร็งหลายชนิดรวมถึงมะเร็งเต้านม การศึกษานี้มีวัตถุประสงค์เพื่อศึกษาการแสดงออกของ MCM2 ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดเพื่อทำนายการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ในมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู วิธีการศึกษา ทบทวนเวชระเบียนอิเล็กทรอนิกส์ของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูที่ได้รับยาเคมีบำบัดก่อนผ่าตัดในโรงพยาบาลจุฬาลงกรณ์ ระหว่าง 1 มกราคม 2558 - 31 ธันวาคม 2563 นำชิ้นเนื้อก่อนได้รับยาเคมีบำบัดตรวจการแสดงออกของ MCM2 ด้วยการย้อมอิมมูโนฮิสโตเคมีและทบทวนลักณะทางพยาธิวิทยาเพื่อคำนวน RCB index ในชิ้นเนื้อหลังได้รับยาเคมีบำบัด คำนวนการติดสีของ MCM2 ด้วยโปรแกรมวิเคราะห์ภาพ Aperio Imagescope ผลการวิจัย ผู้ป่วยทั้งหมด 88 คน มีค่ามัธยฐานอายุ 49 ปี (พิสัย 27-80 ปี) 61.4% เป็นวัยก่อนหมดประจำเดือน มีระยะโรคมะเร็งทางคลินิก cT4 และ cN1 40% และ 66.7% ตามลำดับ เป็นมะเร็งเต้านมชนิด invasive ductal carcinoma 91.1% มีระดับทางพยาธิวิทยาระดับที่ 2 58.9% มีการแสดงออกของ ER >10% 92.2% และสถานะ PR เป็นลบ 22.2% ในชิ้นเนื้อมะเร็งเต้านมทั้งหมด 72 ชิ้นที่สามารถย้อม MCM2 และประเมิน RCB index ได้ พบว่าการแสดงออกของ MCM2 >=40% มีความสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I อย่างมีนัยสำคัญทางสถิติจากการวิเคราะห์แบบ univariate analysis (OR = 18.33 ช่วงความเชื่อมั่น 95% = 1.88-178.98 p-value = 0.012) ปัจจัยทางพยาธิวิทยาคลินิกที่พบว่ามีความสัมพันธ์กับ MCM2 >=40% คือ ระดับของพยาธิวิทยาระดับที่ 3 การแสดงออกของ ER <=10% สถานะ PR เป็นลบ และ Ki-67 >=20% แต่ในการวิเคราะห์แบบ multivariate analysis ไม่มีปัจจัยทางพยาธิวิทยาคลินิกใดสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ลักษณะทางอิมมูโนฮิสโตเคมีก่อนและหลังให้ยาเคมีบำบัดมีลักษณะโดยรวมใกล้เคียงกัน ค่ามัธยฐานของการปลอดโรค และอัตราการปลอดโรคที่ 3 ปี ไม่มีความแตกต่างกันอย่างมีนัยสำคัญทางสถิติระหว่างผู้ป่วยที่มีการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I และ RCB II/III สรุปผลการวิจัย การแสดงออกของ MCM2 >=40% ด้วยการตรวจทางอิมมูโนฮิสโตเคมี ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู มีความสัมพันธ์กับการตอบสนองแบบ RCB 0/I การตรวจการแสดงออกของ MCM2 อาจช่วยทำนายประโยชน์ของยาเคมีบำบัดก่อนผ่าตัดในผู้ป่วยกลุ่มนี้ได้ อย่างไรก็ตามควรมีการศึกษาเพิ่มเติมในประชากรที่มีขนาดใหญ่ขึ้นเพื่อยืนยันผลของการศึกษานี้

Breast cancer is the second most dominant kind of cancer among women. Breast Ultrasound images (BUI) are commonly employed for the detection and classification of abnormalities that exist in the breast. The ultrasound images are necessary to develop artificial intelligence (AI) enabled diagnostic support technologies. For improving the detection performance, Computer Aided Diagnosis (CAD) models are useful for breast cancer detection and classification. The current advancement of the deep learning (DL) model enables the detection and classification of breast cancer with the use of biomedical images. With this motivation, this article presents an Aquila Optimizer with Bayesian Neural Network for Breast Cancer Detection (AOBNN-BDNN) model on BUI. The presented AOBNN-BDNN model follows a series of processes to detect and classify breast cancer on BUI. To accomplish this, the AOBNN-BDNN model initially employs Wiener filtering (WF) related noise removal and U-Net segmentation as a pre-processing step. Besides, the SqueezeNet model derives a collection of feature vectors from the pre-processed image. Next, the BNN algorithm will be utilized to allocate appropriate class labels to the input images. Finally, the AO technique was exploited to fine-tune the parameters related to the BNN method so that the classification performance is improved. To validate the enhanced performance of the AOBNN-BDNN method, a wide experimental study is executed on benchmark datasets. A wide-ranging experimental analysis specified the enhancements of the AOBNN-BDNN method in recent techniques.

Early detection and precise diagnosis of breast cancer (BC) plays an essential part in enhancing the diagnosis and improving the breast cancer survival rate of patients from 30 to 50%. Through the advances of technology in healthcare, deep learning takes a significant role in handling and inspecting a great number of X-ray, MRI, CTR images. The aim of this study is to propose a deep learning model (BCCNN) to detect and classify breast cancers into eight classes: benign adenosis (BA), benign fibroadenoma (BF), benign phyllodes tumor (BPT), benign tubular adenoma (BTA), malignant ductal carcinoma (MDC), malignant lobular carcinoma (MLC), malignant mucinous carcinoma (MMC), and malignant papillary carcinoma (MPC). Breast cancer MRI images were classified into BA, BF, BPT, BTA, MDC, MLC, MMC, and MPC using a proposed Deep Learning model with additional 5 fine-tuned Deep learning models consisting of Xception, InceptionV3, VGG16, MobileNet and ResNet50 trained on ImageNet database. The dataset was collected from Kaggle depository for breast cancer detection and classification. That Dataset was boosted using GAN technique. The images in the dataset have 4 magnifications (40X, 100X, 200X, 400X, and Complete Dataset). Thus we evaluated the proposed Deep Learning model and 5 pre-trained models using each dataset individually. That means we carried out a total of 30 experiments. The measurement that was used in the evaluation of all models includes: F1-score, recall, precision, accuracy. The classification F1-score accuracies of Xception, InceptionV3, ResNet50, VGG16, MobileNet, and Proposed Model (BCCNN) were 97.54%, 95.33%, 98.14%, 97.67%, 93.98%, and 98.28%, respectively. Dataset Boosting, preprocessing and balancing played a good role in enhancing the detection and classification of breast cancer of the proposed model (BCCNN) and the fine-tuned pre-trained models' accuracies greatly. The best accuracies were attained when the 400X magnification of the MRI images due to their high images resolution.

Globally, breast cancer represents the leading cancer type, with millions of women impacted annually. The success of breast cancer treatment relies heavily on timely detection and precise tumor classification. The classification of breast cancer has gained considerable importance in Deep Learning (DL) and medical research with the development of medical imaging techniques, like histopathological imaging. Many existing DL schemes suffer from overfitting and endure difficulties in effectively mining the key features from high-resolution images with subtle variations. Hence, the Xception Convolutional Deep Maxout Network (Xcov-DMN) is developed to classify breast cancer. At the initial stage, the Mean-Shift Filter is applied to the input histopathological image. Following this, the White Blood Cell Network (WBC-Net) is employed for blood cell segmentation with the Balanced Cross-Entropy (BCE) and Focal Loss for ensuring precise segmentation. Next, Colored Histograms, shape features, Haralick Texture Features, and Complete Local Binary Pattern (CLBP) features are excerpted. Consequently, the developed Xcov-DMN is utilized to classify breast cancer. Xcov-DMN is the combination of the Deep Maxout Network (DMN), Fractional Calculus (FC), and Xception Convolutional Neural Network (XCovNet). Moreover, with learning data at 90%, the Xcov-DMN achieved the highest accuracy of 92.755%, True Negative Rate (TNR) of 91.977%, and True Positive Rate (TPR) of 94.765%.

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.

Background: The role of HER2 expression in HER2 negative breast cancer is evolving. Whether HER2 expression has an impact on ER positive, HER2 negative early-stage breast cancer in unclear. Oncotype DX assay Recurrence Score comprises of standardized quantitative HER2 mRNA expression levels by RT-PCR. The objective of this study was to investigate the impact of Oncotype HER2 score on tumor characteristics and outcome. Methods: All women assigned to the Clalit Health Services registry between 2006 to 2011 with ER positive, HER2 negative early-stage breast cancer whose tumor were sent for oncotype were identified. Patients in which HER2 score was reported in the oncotype results were included. Differences in age, tumor characteristics and chemotherapy administration between lower HER2 score (H1), i.e. HER2 score ≤ 9.1, to higher HER2 score (H2), i.e. 9.2-10.7, were analyzed. 5-year Kaplan–Meier for distant recurrence free survival (DRFS) and overall survival (OS) were estimated and differences between H1 and H2 were evaluated. Analyses were repeated separately for node negative and for node positive (N mic and 1-3 lymph nodes) disease. Results: A total of 1535 patients were included, 948 patients with node negative disease and 587 with node positive disease. Of these, 814 and 721 patients were categorized to the H1 and H2 groups, respectively. Median follow-up was 5.4 years. The distribution of oncotype RS was different, with significantly lower proportion of patients with RS between 0-10 and higher proportion of RS above 25 in the H1 group compared to the H2 groups. This difference was identified both for patients with node negative and node positive disease (Table). The distributions of age, tumor size and histological subtype were comparable between the H1 and the H2 groups, both for node negative and node positive disease. Within each RS group, administration of chemotherapy was comparable between H1 and H2 groups, regardless of nodal involvement. DRFS and OS were not associated with HER2 score. For RS 0-25, node negative disease, 5-year DRFS was 96.2% in H1 and 96.4% in H2 groups (p=0.92) and 5-year OS was 97.2% in H1 and 98.1% in H2 groups (p=0.39). For RS >25, node negative disease, 5-years DRFS was 89% in H1 and 90.8% in H2 groups (p=0.33) and 5-year OS was 90.6% in H1 and 90.8% in H2 groups (p=0.7). Similarly, for node positive disease, DRFS and OS were comparable between H1 and H2 group in all subgroups. Conclusions: Low HER2 mRNA expression by Oncotype RT-PCR assay was associated with significantly high Oncotype RS. Patients' outcome was not directly affected by HER2 score and was determined by RS. Further study is desired to clarify the role of HER2 expression in early stage, luminal breast cancer. Table. Comparison of age and tumor characteristics between low (H1) and high (H2) HER2 score Citation Format: Hadar Goldvaser, Raz Mutai, Salomon Stemmer, Iryna Kuchuk, Margarita Tokar, Shani Paluch-Shimon, Rinat Yerushalmi, Karen Drumea, Ella Evron, Amir Sonnenblick, Einav Gal-Yam, Gil Bar Sela, Ayelet Shai, Rotem Merose, Avital Bareket-Samish, Lior Soussan-Gutman. The impact of Human Epidermal growth factor Receptor 2 (HER2) expression by RT-PCR on tumor characteristics and outcome in estrogen receptor (ER) positive, HER2 negative early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-08.

Breast ultrasound image classification and physiological assessment based on GoogLeNet