Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.     

融合空间和通道特征的高精度乳腺癌分类方法

组织病理学图像是鉴别乳腺癌的黄金标准,所以对乳腺癌组织病理学图像的自动、精确的分类具有重要的临床应用价值。为了提高乳腺组织病理图像的分类准确率,从而满足临床应用的需求,提出了一种融合空间和通道特征的高精度乳腺癌分类方法。该方法使用颜色归一化来处理病理图像并使用数据增强扩充数据集,基于卷积神经网络（CNN）模型DenseNet和压缩和激励网络（SENet）融合病理图像的空间特征信息和通道特征信息,并根据压缩-激励（SE）模块的插入位置和数量,设计了三种不同的BCSCNet模型,分别为BCSCNetⅠ、BCSCNetⅡ、BCSCNetⅢ。在乳腺癌癌组织病理图像数据集（BreaKHis）上展开实验。通过实验对比,先是验证了对图像进行颜色归一化和数据增强能提高乳腺的分类准确率,然后发现所设计的三种乳腺癌分类模型中精度最高为BCSCNetⅢ。实验结果表明,BCSCNetⅢ的二分类准确率在99.05%~99.89%,比乳腺癌组织病理学图像分类网络（BHCNet）提升了0.42个百分点;其多分类的准确率在93.06%~95.72%,比BHCNet提升了2.41个百分点。证明了BCSCNet能准确地对乳腺癌组织病理图像进行分类,同时也为计算机辅助乳腺癌诊断提供了可靠的理论支撑。     

Automatic nipple detection in breast thermograms

Breast cancer is one of the most dangerous diseases for women. Detecting breast cancer in its early stage may lead to a reduction in mortality. Although the study of mammographies is the most common method to detect breast cancer, it is outperformed by the analysis of thermographies in dense tissue (breasts of young women). In the last two decades, several computer-aided diagnosis (CAD) systems for the early detection of breast cancer have been proposed. Breast cancer CAD systems consist of many steps, such as segmentation of the region of interest, feature extraction, classification and nipple detection. Indeed, the nipple is an important anatomical landmark in thermograms. The location of the nipple is invaluable in the analysis of medical images because it can be used in several applications, such as image registration and modality fusion. This paper proposes an unsupervised, automatic, accurate, simple and fast method to detect nipples in thermograms. The main stages of the proposed method are: human body segmentation, determination of nipple candidates using adaptive thresholding and detection of the nipples using a novel selection algorithm. Experiments have been carried out on a thermograms dataset to validate the proposed method, achieving accurate nipple detection results in real-time. We also show an application of the proposed method, breast cancer classification in dynamic images, where the new nipple detection technique is used to segment the region of the two breasts from the infrared image. A dataset of dynamic thermograms has been used to validate this application, achieving good results.     

结合深度学习与特征多尺度融合的微钙化簇检测

为了准确识别X线图像中的微钙化簇以进行乳腺癌的辅助诊断与早期预防,结合细粒度级联增强网络(FCE-Net)与多尺度特征融合算法(MFF),提出微钙化簇目标检测方法.首先构建FCE-Net累加卷积模块层级权重,并增强多分支结构,得到细粒度卷积特征图.然后构建MFF候选检测网络,通过二倍上采样融合多尺度特征,得到目标置信度和区域坐标.最后在感兴趣区域池化层分类目标并调整边界框.在MIAS数据集上实验表明,结合FCE-Net与MFF可以提升微小目标的深层特征提取能力,同时增强目标分类与定位的准确度.     

Support vector machines combined with feature selection for breast cancer diagnosis

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. Research efforts have reported with increasing confirmation that the support vector machines (SVM) have greater accurate diagnosis ability. In this paper, breast cancer diagnosis based on a SVM-based method combined with feature selection has been proposed. Experiments have been conducted on different training-test partitions of the Wisconsin breast cancer dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of the method is evaluated using classification accuracy, sensitivity, specificity, positive and negative predictive values, receiver operating characteristic (ROC) curves and confusion matrix. The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.     

HE染色乳腺癌组织病理图像癌巢与间质分割研究

HE染色的乳腺癌组织病理图像是分析诊断乳腺癌常用的方法,病理学家普遍认为癌巢和间质的病理形态学特征对研究乳腺癌的生物学行为有着预示作用,所以准确分割癌巢和间质显得尤为重要。对于HE染色乳腺癌组织病理图像,视癌巢和间质的分割为图像中像素点的分类问题,提取并分析特征,选取最佳特征组合,然后分类为癌巢或间质,并结合间隔采样、归一化与阈值法。实验表明,该方法能较为准确地分割出癌巢和间质,保证较高准确率和精度,在时间效率上能达到较为满意的结果。     

Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images. The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection. This proposed model is a multi-view-based computer-aided diagnosis (CAD) model, which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique (MLO) and cranio-caudal (CC). The digital mammogram images are collected from the digital database for screening mammography (DDSM) dataset. In preprocessing, median filter and contrast limited adaptive histogram equalization (CLAHE) techniques are utilized for image enhancement. After preprocessing, the segmentation is performed using the region growing (RG) algorithm. The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients (PHOG) and the AlextNet model. Finally, the classification is performed using the weighted k-nearest neighbor (WkNN) optimized with sequential minimal optimization (SMO). The classified images are evaluated based on accuracy, recall, precision, specificity, f1-score, and mathews correlation coefficient (MCC). Additionally, the false positive and error rates are evaluated. The proposed model obtained 98.57% accuracy, 98.61% recall, 99.25% specificity, 98.63% precision, 97.93% f1-score, 96.26% MCC, 0.0143 error rate, and 0.0075 false positive rate (FPR). Compared to the existing models, the research model has obtained better performances and outperformed the other models.     

Early diagnosis of breast cancer by gene expression profiles

Breast cancer is the second leading cause of cancer death in women worldwide. Nevertheless, there is evidence that early detection and treatment can increase the survival rate of breast cancer patients. This paper presents an intelligent decision support system (IDSS) for breast cancer diagnosis by using gene expression profiles. The proposed system first extracts significant features from the input patterns by using information gain and then employs deep genetic algorithm for feature reduction as well as for breast cancer diagnosis. The proposed system is evaluated by considering a benchmark microarray dataset and compared with the most recent systems. The results show that the proposed IDSS outperforms other systems in terms of diagnosis time and accuracy. The proposed system produces 100 % classification accuracy. In addition, the proposed system reduces the required memory space.     

改进Xception模型的乳腺钼靶图像识别研究

乳腺X线摄影技术是早期发现乳腺癌的主要方法,但其结果很大程度上受放射科医师临床诊断经验的限制;基于卷积神经网络对乳腺钼靶图像自动分类的研究可以为放射科医师临床诊断提供意见,然而乳腺癌肿块边缘模糊且良恶性肿块特征差异较小,分类任务面临重重挑战;为了提高乳腺钼靶图像分类的准确率,提出一种基于Xception模型的改进优化算法,改进模型中的残差连接模块,并嵌入Squeeze-and-excitation（SE）注意力机制对模型进行优化;采用优化后的Xception模型并结合迁移学习算法进行乳腺钼靶图像特征提取,并优化全连接层网络进行图像分类,使用公开的乳腺癌图像数据库CBIS-DDSM进行实验,将乳腺钼靶图像自动分为良性和恶性;实验结果表明该方法可以有效提高模型的分类效果,准确率和AUC分别达到了97.46%和99.12%。     

Hepatitis disease diagnosis using a new hybrid system based on feature selection (FS) and artificial immune recognition system with fuzzy resource allocation

This paper presents a novel method for diagnosis of hepatitis disease. The proposed method is based on a hybrid method that uses feature selection (FS) and artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism. AIRS has showed an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabets, liver disorders classification. By hybridizing FS and AIRS with fuzzy resource allocation mechanism, a method is obtained to solve this diagnosis problem via classifying. The robustness of this method with regard to sampling variations is examined using a cross-validation method. We used hepatitis disease dataset which is taken from UCI machine learning repository. We obtained a classification accuracy of 92.59%, which is the highest one reached so far. The classification accuracy was obtained via 10-fold cross validation. The obtained classification accuracy of our system was 92.59% and it was very promising with regard to the other classification applications in literature for this problem. Also, sensitivity, and specificity values for hepatitis disease dataset were obtained as 100 and 85%.     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

     

Development of Efficient Classification Systems for the Diagnosis of Melanoma

Skin cancer is usually classified as melanoma and non-melanoma. Melanoma now represents 75% of humans passing away worldwide and is one of the most brutal types of cancer. Previously, studies were not mainly focused on feature extraction of Melanoma, which caused the classification accuracy. However, in this work, Histograms of orientation gradients and local binary patterns feature extraction procedures are used to extract the important features such as asymmetry, symmetry, boundary irregularity, color, diameter, etc., and are removed from both melanoma and non-melanoma images. This proposed Efficient Classification Systems for the Diagnosis of Melanoma (ECSDM) framework consists of different schemes such as preprocessing, segmentation, feature extraction, and classification. We used Machine Learning (ML) and Deep Learning (DL) classifiers in the classification framework. The ML classifier is Naïve Bayes (NB) and Support Vector Machines (SVM). And also, DL classification framework of the Convolution Neural Network (CNN) is used to classify the melanoma and benign images. The results show that the Neural Network (NNET) classifier’ achieves 97.17% of accuracy when contrasting with ML classifiers.     

RETRACTED ARTICLE: An improved data mining technique for classification and detection of breast cancer from mammograms

The high incidence of breast cancer in women has increased significantly in the recent years. Mammogram breast X-ray imaging is considered the most effective, low-cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15–30% of masses referred for surgical biopsy are actually malignant. Physician experience of detecting breast cancer can be assisted by using some computerized feature extraction and classification algorithms. Computer-aided classification system was used to help in diagnosing abnormalities faster than traditional screening program without the drawback attribute to human factors. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 26 features, and we found that these features are capable of differentiating between normal and cancerous breast tissues in order to minimize the classification error. The third step is the classification process; we used the technique of the association rule mining to classify between normal and cancerous tissues. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

     

人型网络乳腺钼靶影像分类方法研究

乳腺癌是女性最常见的恶性肿瘤之一,严重威胁患者健康,因此乳腺钼靶图像多分类对临床诊断乳腺癌具有十分重要的作用。传统卷积神经网络直接采用高级特征对乳腺钼靶图像进行多分类研究,此方法准确率不高。为了进一步提高分类准确率,构建人型网络模型进行分类。此结构通过堆叠的卷积层以及最大池化层来进行图片的低级特征进行提取,通过堆叠的卷积层以及上池化层将特征逐步返回到图片形式的特征图,通过堆叠的卷积层以及最大池化层再次提取到更高级的特征并与之前的低级特征进行级联,将级联的特征经过全局最大池化层进行池化并得到最终分类。在中山大学肿瘤防治中心的1 824幅乳腺钼靶图像做仿真实验,实验结果表明,该方法的准确率达到了74.54%,优于现有相关网络模型。     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

A deep ensemble network model for classifying and predicting breast cancer

Breast cancer is one of the leading causes of death among women worldwide. In most cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates due to breast cancer. Breast cancer can be diagnosed by classifying tumors. There are two different types of tumors, such as malignant and benign tumors. Identifying the type of tumor is a tedious task, even for experts. Hence, an automated diagnosis is necessary. The role of machine learning in medical diagnosis is eminent as it provides more accurate results in classifying and predicting diseases. In this paper, we propose a deep ensemble network (DEN) method for classifying and predicting breast cancer. This method uses a stacked convolutional neural network, artificial neural network and recurrent neural network as the base classifiers in the ensemble. The random forest algorithm is used as the meta-learner for providing the final prediction. Experimental results show that the proposed DEN technique outperforms all the existing approaches in terms of accuracy, sensitivity, specificity, F-score and area under the curve (AUC) measures. The analysis of variance test proves that the proposed DEN model is statistically more significant than the other existing classification models; thus, the proposed approach may aid in the early detection and diagnosis of breast cancer in women, hence aiding in the development of early treatment techniques to increase survival rate.     

改进DenseNet的乳腺癌病理图像八分类研究

目前,在医学图像领域存在乳腺癌组织病理图像自动分类难以应用于临床诊断的现象,究其根源是当前没有大型公开的数据集或数据集数据不均衡。针对上述问题,提出一种结合密集卷积神经网络（dense convolutional network,DenseNet）、注意力机制（attention mecheanism）和焦点损失函数（Focal loss）的乳腺癌组织病理图像的多分类模型,即DAFLNet。DAFLNet在乳腺癌组织病理图像数据集BreaKHis上进行训练、验证与测试,最终实验结果显示,该模型对良恶性二分类的识别准确率达到99.1%,对乳腺亚型八分类的识别准确率达到95.5%。证明在数据不均衡的条件下,DAFLNet模型能够准确地对乳腺组织病理图像进行八分类。     

基于自联想神经网络的良恶性肿瘤分类技术

尽管乳腺癌的诊断和处理技术在不断进步,但乳腺病灶的早期检测仍然是阻止癌症的主要方法。乳腺组织中肿块的存在是乳腺癌的重要特征。通过使用自联想神经网络和多层感知器技术研究了良恶性肿瘤的分类方法。该研究的实验结果显示,在DDSM数据库上进行训练和测试,得到了较高的CAD系统的灵敏度（TP）和较低的假阳性率（FP）;在100%的训练分类率上获得了91%的测试分类率;ROC曲线下方面积最大可达约0.948。     

Histology image analysis for carcinoma detection and grading

This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.     

Automated skin lesion detection and classification using fused deep convolutional neural network on dermoscopic images

Skin cancer becomes a deadly disease that affect people of all ages globally. The availability of various types of benign and malignant melanoma makes the skin lesion diagnostic process difficult. Since the visual inspection of skin cancer is costlier and lengthy process, it is needed to design automatic diagnosis model to classify skin lesions accurately and promptly. Computer-aided diagnosis models can be employed to identify the presence of skin lesions using dermoscopic images. The automatic identification of skin lesions can assist the doctors and enable the detection process at an efficient and faster rate. With this motivation, this article presents an automated skin lesion detection and classification using fused deep convolutional neural network (ASDC-FDCNN) on dermoscopic images. The ASDC-FDCNN technique aims to identify the existence of skin lesions from dermoscopic images. The ASDC-FDCNN model involves the design of two deep learning models namely VGG19 and ResNet152 models. Besides, the fusion based feature extraction process is performed to derive feature vectors. In addition, the DCNN technique was employed as classifier for identifying the presence or absence of skin lesions. The performance validation of the ASDC-FDCNN technique takes place utilizing benchmark skin lesion dataset. A comparative results analysis reported the better performance of the ASDC-FDCNN model over the recent technique with respect to various measures.