Accurate Diagnosis Of Breast Cancer Research Articles

Method to improve the localization accuracy and contrast recovery of lesions in separately acquired X-ray and diffuse optical tomographic breast imaging.

Near-infrared diffuse optical tomography (DOT) has the potential to improve the accuracy of breast cancer diagnosis and aid in monitoring the response of breast tumors to chemotherapy by providing hemoglobin-based functional imaging. The use of structural lesion priors derived from clinical breast imaging methods, such as mammography, can improve recovery of tumor optical contrast; however, accurate lesion prior placement is essential to take full advantage of prior-guided DOT image reconstruction. Simultaneous optical and anatomical imaging may not always be possible or desired, which can make the accurate registration of the lesion prior challenging. In this paper, we present a three-step lesion prior scanning approach to facilitate improved accuracy in lesion localization based on the optical contrast quantified by the total hemoglobin concentration (HbT) for non-simultaneous multimodal DOT and digital breast tomosynthesis (DBT) imaging. In three challenging breast cancer patient cases, where no clear optical contrast was present initially, we have demonstrated consistent improvement in the recovered HbT lesion contrast by utilizing this method.

Automatic Detection System to Identify Invasive Ductal Carcinoma by Predicting Bloom Richardson Grading from Histopathological Images

After skin cancer, the most common type of cancer is breast cancer among the world population. Breast cancer is the leading cause of cancer-induced mortality among women. Breast cancer is frequently diagnosed by using biopsies in which tissue is removed from the breast and studied under a microscope. The results of these biopsies are based on the qualifications and experience of the pathologist who diagnoses the abnormal cell under the microscope. With the emergence of advancements in the fields of image processing and artificial intelligence, there is an area of interest in developing a deep learning model to improve and enhance the quality and accuracy of breast cancer diagnosis. This study proposed a deep learning model that automatically analyses the multiclass classification of hematoxylin and eosin-stained histological images of invasive ductal carcinoma (IDC) by discriminating the IDC into grades such as G-1, G-2, and G-3. The methodology is focused on a deep learning model to detect grades of invasive ductal carcinoma by adopting the Sequential Convolutional Neural Network Two-Dimensional (CNN2D). We used DataBiox, a public dataset taken from an internet source consisting of 922 images. We evaluate the result of multiclass classification by dividing 80% and 20% of the dataset into training and testing data, respectively. As a result of the training and testing of the pre-trained CNN model, sequential CNN yields the accuracy of the model of 92.81%. Our model accurately classifies a multi-class classification of histological images of grades of breast cancer, specifically IDC. It is ready to be tested with a more diverse and massive database in the future.

Breast Cancer Detection Using Machine Learning Techniques

Abstract: Cancer is one of the most prominent cause of fatalities around the world, accounting over 1 crore deaths in past year out of which 22.6% deaths were due to Breast cancer (BC). BC is the most common type of cancer among women, it accounts for 14.7 % of cancer cases in India. Multiple pieces of research have been conducted over the early detection of BC that can help begin treatment on time thus decreasing the mortality rate. Out of the total diagnosed, only about 86% are diagnosed correctly. Biopsy images of cells have a risk of false detection which may endanger the life of a person. There is a dire need of discovering new alternative methods that have an easy implementation with different data sets, are cost-effective, reliable, and safer, that can give an accurate prediction. This paper proposes a model combined with several Machine Learning algorithms(ML) that are Decision Trees, Artificial Neural Network, K-Nearest Neighbour, Support Vector Machine for an effective and accurate breast cancer diagnosis. Index Terms - Breast Cancer; Breast Cancer Detection; Machine Learning Key Words: Breast Cancer (BC); Artificial neural networks (ANN); Wisconsin breast cancer data set.

Multi-Class Classification of Breast Cancer Using 6B-Net with Deep Feature Fusion and Selection Method.

Breast cancer has now overtaken lung cancer as the world’s most commonly diagnosed cancer, with thousands of new cases per year. Early detection and classification of breast cancer are necessary to overcome the death rate. Recently, many deep learning-based studies have been proposed for automatic diagnosis and classification of this deadly disease, using histopathology images. This study proposed a novel solution for multi-class breast cancer classification from histopathology images using deep learning. For this purpose, a novel 6B-Net deep CNN model, with feature fusion and selection mechanism, was developed for multi-class breast cancer classification. For the evaluation of the proposed method, two large, publicly available datasets, namely, BreaKHis, with eight classes containing 7909 images, and a breast cancer histopathology dataset, containing 3771 images of four classes, were used. The proposed method achieves a multi-class average accuracy of 94.20%, with a classification training time of 226 s in four classes of breast cancer, and a multi-class average accuracy of 90.10%, with a classification training time of 147 s in eight classes of breast cancer. The experimental outcomes show that the proposed method achieves the highest multi-class average accuracy for breast cancer classification, and hence, the proposed method can effectively be applied for early detection and classification of breast cancer to assist the pathologists in early and accurate diagnosis of breast cancer.

Stacking approach for accurate Invasive Ductal Carcinoma classification

The accurate diagnosis of Breast cancer (BC) requires adequately exploiting Artificial intelligence (AI)-based methods in the diagnosing process. To tackle the issue of accurate BC diagnosis, we have proposed a deep learning-based stacking method (StackBC). In particular, we have incorporated deep learning models including Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, Transfer Learning (TL) and Data Augmentation (DA) approaches have been incorporated to balance the dataset and adequately train the model. To further improve the predictive outputs of the model, we used the stacking technique. Among the three individual base classifiers, the performance of the GRU model was better. Hence, we selected the GRU as a meta classifier to distinguish between Non-IDC and IDC breast images. The experimental results confirmed that the StackBC method outperformed state-of-the-art methods.

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.

Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.

Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.

Ultrasonic Omics Based on Intelligent Classification Algorithm in Hormone Receptor Expression and Efficacy Evaluation of Breast Cancer.

The changes of hormone expression and efficacy of breast cancer (BC) were investigated under the VGG19FCN algorithm and ultrasound omics. 120 patients with BC were selected, of which 90 were positive for hormone receptor and 30 were negative. The VGG19FCN model algorithm and classifier were selected to classify the features of ultrasound breast map, and reliable ultrasound feature data were obtained. The evaluation and analysis of BC hormone receptor expression and clinical efficacy in patients with BC were realized by using ultrasonic omics. The evaluation of the results of the VGG19FCN algorithm was DSC (Dice similarity coefficient) = 0.9626, MPA (mean pixel accuracy) = 0.9676, and IOU (intersection over union) = 0.9155. When the classifier was used to classify the lesion features of BC image, the sensitivity of classification was improved to a certain extent. Compared with the classification of radiologists, when classifying whether patients had BC lesions, the sensitivity increased by 22.7%, the accuracy increased from 71.9% to 79.7%, and the specific evaluation index increased by 0.8%. No substantial difference was indicated between RT (arrive time), WIS (wash in slope), and TTP (time to peak) before and after chemotherapy, P > 0.05. After chemotherapy, the AUC (area under curve) and PI (peak intensity) of ultrasonographic examination were substantially lower than those before chemotherapy, and there were substantial differences in statistics (P < 0.05). In summary, the VGG19FCN algorithm effectively reduces the subjectivity of traditional ultrasound images and can effectively improve the value of ultrasound image features in the accurate diagnosis of BC. It provides a theoretical basis for the subsequent treatment of BC and the prediction of biological behavior. The VGG19FCN algorithm had a good performance in ultrasound image processing of BC patients, and hormone receptor expression changed substantially after chemotherapy treatment.

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis.

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.

A Personalized Medical Decision Support System Based on Explainable Machine Learning Algorithms and ECC Features: Data from the Real World.

Artificial intelligence can help physicians improve the accuracy of breast cancer diagnosis. However, the effectiveness of AI applications is limited by doctors’ adoption of the results recommended by the personalized medical decision support system. Our primary purpose is to study the impact of external case characteristics (ECC) on the effectiveness of the personalized medical decision support system for breast cancer assisted diagnosis (PMDSS-BCAD) in making accurate recommendations. Therefore, we designed a novel comprehensive framework for case-based reasoning (CBR) that takes the impact of external features of cases into account, made use of the naive Bayes and k-nearest neighbor (KNN) algorithms (CBR-ECC), and developed a PMDSS-BCAD system by using the CBR-ECC model and external features as system components. Under the new case-based reasoning framework, the accuracy of the combined model of naive Bayes and KNN with an optimal K value of 2 is 99.40%. Moreover, in a real hospital scenario, users rated the PMDSS-BCAD system, which takes into account the external characteristics of the case, better than the original personalized system. These results suggest that PMDSS-BCD can not only provide doctors with more personalized and accurate results for auxiliary diagnosis, but also improve doctors’ trust in the results, so as to encourage doctors to adopt the results recommended by the personalized system.

Multiplexed analysis of small extracellular vesicle-derived mRNAs by droplet digital PCR and machine learning improves breast cancer diagnosis

Breast cancer has become the leading cause of global cancer incidence and a serious threat to women's health. Accurate diagnosis and early treatment are of great importance to prognosis. Although clinically used diagnostic approaches can be used for cancer screening, accurate diagnosis of breast cancer is still a critical unmet need. Here, we report a 4-plex droplet digital PCR technology for simultaneous detection of four small extracellular vesicle (sEV)-derived mRNAs (PGR, ESR1, ERBB2 and GAPDH) in combination with machine learning (ML) algorithms to improve breast cancer diagnosis. We evaluate the diagnsotic results with and without the assistance of the ML models. The results indicate that ML-assisted analysis exhibits higher diagnostic performance even using a single marker for breast cancer diagnosis, and demonstrate improved diagnostic performance under the best combination of biomarkers and suitable ML diagnostic model. Therefore, multiple sEV-derived mRNAs analysis coupled with ML not only provides the best combination of markers for breast cancer diagnosis, but also significantly improves the diagnostic efficiency of breast cancer.

Breast ultrasound image segmentation: A coarse-to-fine fusion convolutional neural network.

Breast ultrasound (BUS) image segmentation plays a crucial role in computer-aided diagnosis systems for BUS examination, which are useful for improved accuracy of breast cancer diagnosis. However, such performance remains a challenging task owing to the poor image quality and large variations in the sizes, shapes, and locations of breast lesions. In this paper, we propose a new convolutional neural network with coarse-to-fine feature fusion to address the aforementioned challenges. The proposed fusion network consists of an encoder path, a decoder path, and a core fusion stream path (FSP). The encoder path is used to capture the context information, and the decoder path is used for localization prediction. The FSP is designed to generate beneficial aggregate feature representations (i.e., various-sized lesion features, aggregated coarse-to-fine information, and high-resolution edge characteristics) from the encoder and decoder paths, which are eventually used for accurate breast lesion segmentation. To better retain the boundary information and alleviate the effect of image noise, we input the superpixel image along with the original image to the fusion network. Furthermore, a weighted-balanced loss function was designed to address the problem of lesion regions having different sizes. We then conducted exhaustive experiments on three public BUS datasets to evaluate the proposed network. The proposed method outperformed state-of-the-art (SOTA) segmentation methods on the three public BUS datasets, with average dice similarity coefficients of 84.71(±1.07), 83.76(±0.83), and 86.52(±1.52), average intersection-over-union values of 76.34(±1.50), 75.70(±0.98), and 77.86(±2.07), average sensitivities of 86.66(±1.82), 85.21(±1.98), and 87.21(±2.51), average specificities of 97.92(±0.46), 98.57(±0.19), and 99.42(±0.21), and average accuracies of 95.89(±0.57), 97.17(±0.3), and 98.51(±0.3). The proposed fusion network could effectively segment lesions from BUS images, thereby presenting a new feature fusion strategy to handle challenging task of segmentation, while outperforming the SOTA segmentation methods. The code is publicly available at https://github.com/mniwk/CF2-NET.

Breast cancer: understanding etiology, addressing molecular signaling pathways, identifying therapeutic targets and strategizing the treatment

Breast cancer has messed the life of a greater number of women being the most common cancer affecting them worldwide. A number of risk factors contribute the breast malignancy, however, genetic drift is accountable the most. Depending on the cell origin, invasiveness and receptors involved, breast cancer is classified into various subtypes. The accurate diagnosis of breast cancer is important as it defines the prognosis and directs the type of treatment required. A number of major signaling pathways involved in breast tumorigenesis and its development include estrogen receptors (ERs), HER2, Wnt/β-catenin, Notch, Hedgehog (Hh), PI3K and mTOR pathway. Furthermore, certain enzymes like Cyclin dependent kinases and breast tumor kinases also play a vital role in cell cycle regulation and therefore, in the development of breast neoplasms. Recent studies have also enlightened the role of non-coding RNAs in breast cancer development. This review discusses various aspects of breast cancer such as its etiology, subtypes, various signaling pathways involved, targets projected by these pathways and the current treatment options based on a few of these targets. Also, the role of different genes, enzymes and non-coding RNAs related to breast tumorigenesis and development is discussed.

Towards Development of LED-Based Time-Domain Near-IR Spectroscopy System for Delineating Breast Cancer From Adjacent Normal Tissue

Currently, the breast cancer diagnosis is performed by microscopic examination of thin slices of ex-vivo biopsy tissue by the pathologist. In this paper, a robust and affordable LED-based time-domain near-infrared spectroscopy system (Spectro-IRTDx) is proposed to further aid the pathologist for rapid and accurate breast cancer diagnosis. The measurements are performed on formalin-fixed cancerous tissues (invasive ductal carcinoma) and their adjacent normal tissues obtained from N = 6 patients. The mean effective attenuation coefficient ( μ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>eff</i></sub> ) for cancerous tissue is found to be 5.41 and 2.41 times higher than adjacent normal tissue, with good statistical significance (p = 0.00216) while operating at 850 nm and 940 nm, respectively. The average detected peak voltage (DPV) for adjacent normal tissue is found to be 3.44 and 4.16 times higher than cancerous tissue, with good statistical significance (p = 0.00216) while operating at 850 nm and 940 nm, respectively. The mean time of flight for cancerous tissue was 46 ns and 4 ns higher than the adjacent normal tissue while operating at 850 nm and 940 nm, respectively, with good statistical significance (p = 0.00216) at 850 nm. The obtained results establish the proof-of-concept of the time-domain near-infrared spectroscopy for rapid diagnosis of core biopsy tissues.

Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis.

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg–Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

Cross-modality image feature fusion diagnosis in breast cancer

Considering the complementarity of mammography and breast MRI, the research of feature fusion diagnosis based on cross-modality images was explored to improve the accuracy of breast cancer diagnosis. 201 patients with both mammography and breast MRI were collected retrospectively, including 117 cases of benign lesions and 84 cases of malignant ones. Two feature optimization strategies of sequential floating forward selection (SFFS), SFFS-1 and SFFS-2, were defined based on the sequential floating forward selection method. Each strategy was used to analyze the diagnostic performance of single-modality images and then to study the feature fusion diagnosis of cross-modality images. Three feature fusion approaches were compared: optimizing MRI features and then fusing those of mammography; optimizing mammography features and then fusing those of MRI; selecting the effective features from the whole feature set (mammography and MRI). Support vector machine, Naive Bayes, and K-nearest neighbor were employed as the classifiers and were finally integrated to get better performance. The average accuracy and area under the ROC curve (AUC) of MRI (88.56%, 0.9 for SFFS-1, 88.39%, 0.89 for SFFS-2) were better than mammography (84.25%, 0.84 for SFFS-1, 80.43%, 0.80 for SFFS-2). Furthermore, compared with a single modality, the average accuracy and AUC of cross-modality feature fusion can improve from 85.40% and 0.86 to 89.66% and 0.91. Classifier integration improved the accuracy and AUC from 90.49%, 0.92 to 92.37%, and 0.97. Cross-modality image feature fusion can achieve better diagnosis performance than a single modality. Feature selection strategy SFFS-1 has better efficiency than SFFS-2. Classifier integration can further improve diagnostic accuracy.

Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.

Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.

Evaluation Of Machine Learning Classifiers In Breast Cancer Diagnosis

Breast Cancer is one among the most deadly diseases that threaten women. It affects women in general but men are also not exceptions to it. Most breast cancers end up fatal except for a few cases. Early diagnosis of the disease helps in successful treatment and cure. Computer Aided Detection (CADe) or Computer Aided Diagnosis (CADx) System helps physicians to take prompt decisions in the field of medical imaging. CAD systems are aimed at identifying the abnormalities at the earliest in the human body which a human professional may fail to detect. Machine learning techniques in the field of medical imaging are increasingly being used in the accurate diagnosis of breast cancer. Machine learning classifiers such as Support Vector Machine and Neural Network are examined in this paper. Breast mammogram images, both normal and pathological images were used in this experiment. The machine learning classifiers were employed to identify the given image as either Benign ore Malignant. Performance of both the classifiers was recorded and it was observed that the Neural Network classifier excelled in the diagnosis with 98% accuracy than the Support Vector Machine classifier.

Deep learning in mammography images segmentation and classification: Automated CNN approach

In this work, a new framework for breast cancer image segmentation and classification is proposed. Different models including InceptionV3, DenseNet121, ResNet50, VGG16 and MobileNetV2 models, are applied to classify Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and the Curated Breast Imaging Subset of DDSM (CBIS-DDSM) into benign and malignant. Moreover, the trained modified U-Net model is utilized to segment breast area from the mammogram images. This method will aid as a radiologist's assistant in early detection and improve the efficiency of our system. The Cranio Caudal (CC) vision and Mediolateral Oblique (MLO) view are widely used for the identification and diagnosis of breast cancer. The accuracy of breast cancer diagnosis will be improved as the number of views is increased. Our proposed frame work is based on MLO view and CC view to enhance the system performance. In addition, the lack of tagged data is a big challenge. Transfer learning and data augmentation are applied to overcome this problem. Three mammographic datasets; MIAS, DDSM and CBIS-DDSM, are utilized in our evaluation. End-to-end fully convolutional neural networks (CNNs) are introduced in this paper. The proposed technique of applying data augmentation with modified U-Net model and InceptionV3 achieves the best result, specifically with the DDSM dataset. This achieves 98.87% accuracy, 98.88% area under the curve (AUC), 98.98% sensitivity, 98.79% precision, 97.99% F1 score, and a computational time of 1.2134 s on DDSM datasets.