Multi-Input CNN Models for Breast Cancer Detection Using BreakHis Database

Breast cancer is considered the second most common reason for death among women. The gold standard for detecting breast cancer is the visual interpretation of histopathological images, but it is a complicated process that takes years of experience and a lot of skills of the pathologists. Sometimes, the limitations of the visual interpretation and the lack of experience result in the failure of diagnosing breast cancer. So, the computer-aided diagnosis (CAD) system can be taken into consideration as a helping tool to reduce the error of diagnosis of breast cancer. In this paper, a novel approach based on convolutional neural networks is introduced to classify breast cancer from the histopathological images of the breast tissues. In the clinical process of breast cancer diagnosis, pathologists examine the histopathological images of the breast tissue at different magnification levels. In this research, a single convolutional neural networks model is used to take the input of the same image with four different magnification levels parallelly. Our proposed approach outperformed existing state-of-the-art approaches by a substantial margin.

Breast cancer is the most common cancer worldwide with high death rate especially for women, early diagnosis can increase the survival opportunity with correct treatment in the hospital. The Computer-Assisted Diagnosis (CAD) system is of vital significant for improving the diagnostic accuracy as the diagnosis process is tedious and the result may be different between pathologists. In this paper, we proceed research on breast cancer histopathological images classification based on deep Convolutional Neural Network (CNN). The approach proposed in this work utilize CNN to extract features of histopathological images and classify the images into begin tumors and malignant tumors by softmax function. Eliminate overfitting phenomenon by data augmentation and fine tune technology while improving the performance of networks by cross validation training strategy. The experiments in this paper were conducted on BreaKHis database [4] available to scientific study from 2014 and the results proved the algorithm's advantage on accuracy.

Despite the significant progress in diagnosis and treatment, breast cancer still continues to be the most common deadly disease occurring among women in the world. Histopathological analysis is the standard method for breast cancer diagnosis. Manual inspection of histological samples is a highly specialized and time consuming task. Computer-Aided Diagnosis Systems (CAD) could overcome the limitations of manual analysis and assist pathologists to diagnose cancer more accurately and efficiently. Hence, we propose a novel approach that uses deep learned nucleus feature classifier fusion framework for breast cancer detection from histopathological images. Recently, convolutional neural networks (CNNs) have produced high recognition rates compared to hand crafted features but results in much higher complexity in network design. Moreover, designing CNN from scratch requires large amount of training data. So, we use pretrained CNNs for feature extraction. Extracted features are fused using norm pooling and fed to Support Vector Machine (SVM) for classification. Classification results using the different CNNs are fused using belief theory based fusion. Performance analysis is done using accuracy, sensitivity, specificity, receiver operating characteristics (ROC) curve and AUC (Area under the ROC curve) measures. Results comparable to the current state of the art is achieved.

In this study, a multiscale framework combining the Lifting Wavelet Transform (LWT) and multi-path Convolutional Neural Network (CNN) is proposed to enhance the analysis of histopathological breast cancer images. LWT is employed due to its capability to obtain multi-resolution features that effectively retain key textural details. In particular, a multi-path CNN facilitates the concurrent processing of features at multiple levels of wavelet decompositions, thereby preserving diagnostic information that sole-path CNN models may lose. The approach was evaluated on the BreakHis dataset, which contains 7,638 rated images at varying magnifications, achieving 99.34% test accuracy by combining magnification levels using a Haar wavelet filter. Results obtained from comparative analyses against general CNN models confirm that the new strategy performs better, thereby validating the efficacy of integrating wavelet-based textural extraction and state-of-the-art CNN models for early-stage breast cancer diagnosis.

Simple SummaryBreast cancer is one of the leading causes of cancer death among women. Developing machine learning-based diagnosis models receives great attention from researchers and scientists using histopathology images. Deep learning (DL) algorithms automatically extract features from raw data through convolutional operations. The generalization of the DL models’ results relies on large datasets, although they eliminate the expert knowledge in the feature extraction stage. This work aimed to compare the performance of the features extracted via deep learning and a knowledge-based approach in breast cancer detection from histopathology images.Cancer develops when a single or a group of cells grows and spreads uncontrollably. Histopathology images are used in cancer diagnosis since they show tissue and cell structures under a microscope. Knowledge-based and deep learning-based computer-aided detection is an ongoing research field in cancer diagnosis using histopathology images. Feature extraction is vital in both approaches since the feature set is fed to a classifier and determines the performance. This paper evaluates three feature extraction methods and their performance in breast cancer diagnosis. Features are extracted by (1) a Convolutional Neural Network, (2) a transfer learning architecture VGG16, and (3) a knowledge-based system. The feature sets are tested by seven classifiers, including Neural Network (64 units), Random Forest, Multilayer Perceptron, Decision Tree, Support Vector Machines, K-Nearest Neighbors, and Narrow Neural Network (10 units) on the BreakHis 400× image dataset. The CNN achieved up to 85% for the Neural Network and Random Forest, the VGG16 method achieved up to 86% for the Neural Network, and the knowledge-based features achieved up to 98% for Neural Network, Random Forest, Multilayer Perceptron classifiers.

Breast cancer is the prominent foremost disease causing death among the various cancers affecting female. Precise and initial diagnosis of diseases can often save lives. Histopathology microscopy imaging as a best practice used by pathologist to diagnose breast cancer. The procedure is labor intensive and time consuming. Hence, investigation of tissue samples in automated manner from histopathology images has constructive clinical implication in early diagnosis and treatment of disease. To capture discriminating traits from images in better way, images are acquired at various optical magnifications (x40, x100, x200 and x400). The disease can be treated well if the type of actual subclass of benign and malignant categories on a sample image is known early. The research gap observed in current literature isthat, limited study done on magnification independent multi- classification. With recent advances in Artificial Intelligence (AI)especially deep learning, the Convolutional Neural Networks (CNN) can be effectively utilized for automatic histopathology image analysis. In this work, multi-classification framework across different magnification factor using recent tax- onomy of BreakHis dataset is proposed. The proposed approach explores different transfer learning models with fine tuning adaptation. The experiments have shown impressive results on pre-trained models DenseNet121 as well as on DenseNet201. Promising results are observed towards multi-classification of breast cancer histopathology images as compared to other models.

BackgroundHistopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels). The property of extremely large size for a single image also makes a histopathology image dataset be considered large-scale, even if the number of images in the dataset is limited.ResultsIn this paper, we propose leveraging deep convolutional neural network (CNN) activation features to perform classification, segmentation and visualization in large-scale tissue histopathology images. Our framework transfers features extracted from CNNs trained by a large natural image database, ImageNet, to histopathology images. We also explore the characteristics of CNN features by visualizing the response of individual neuron components in the last hidden layer. Some of these characteristics reveal biological insights that have been verified by pathologists. According to our experiments, the framework proposed has shown state-of-the-art performance on a brain tumor dataset from the MICCAI 2014 Brain Tumor Digital Pathology Challenge and a colon cancer histopathology image dataset.ConclusionsThe framework proposed is a simple, efficient and effective system for histopathology image automatic analysis. We successfully transfer ImageNet knowledge as deep convolutional activation features to the classification and segmentation of histopathology images with little training data. CNN features are significantly more powerful than expert-designed features.

Breast cancer continues to be among the leading causes of death for women and much effort has been expended in the form of screening programs for prevention. Given the exponential growth in the number of mammograms collected by these programs, computer-assisted diagnosis has become a necessity. Computer-assisted detection techniques developed to date to improve diagnosis without multiple systematic readings have not resulted in a significant improvement in performance measures. In this context, the use of automatic image processing techniques resulting from deep learning represents a promising avenue for assisting in the diagnosis of breast cancer. In this paper, we present a deep learning approach based on a Convolutional Neural Network (CNN) model for multi-class breast cancer classification. The proposed approach aims to classify the breast tumors in non-just benign or malignant but we predict the subclass of the tumors like Fibroadenoma, Lobular carcinoma, etc. Experimental results on histopathological images using the BreakHis dataset show that the DenseNet CNN model achieved high processing performances with 95.4% of accuracy in the multi-class breast cancer classification task when compared with state-of-the-art models.

Coronavirus (COVID-19) was first observed in Wuhan, China, and quickly propagated worldwide. It is considered the supreme crisis of the present era and one of the most crucial hazards threatening worldwide health. Therefore, the early detection of COVID-19 is essential. The common way to detect COVID-19 is the reverse transcription-polymerase chain reaction (RT-PCR) test, although it has several drawbacks. Computed tomography (CT) scans can enable the early detection of suspected patients, however, the overlap between patterns of COVID-19 and other types of pneumonia makes it difficult for radiologists to diagnose COVID-19 accurately. On the other hand, deep learning (DL) techniques and especially the convolutional neural network (CNN) can classify COVID-19 and non-COVID-19 cases. In addition, DL techniques that use CT images can deliver an accurate diagnosis faster than the RT-PCR test, which consequently saves time for disease control and provides an efficient computer-aided diagnosis (CAD) system. The shortage of publicly available datasets of CT images, makes the CAD system’s design a challenging task. The CAD systems in the literature are based on either individual CNN or two-fused CNNs; one used for segmentation and the other for classification and diagnosis. In this article, a novel CAD system is proposed for diagnosing COVID-19 based on the fusion of multiple CNNs. First, an end-to-end classification is performed. Afterward, the deep features are extracted from each network individually and classified using a support vector machine (SVM) classifier. Next, principal component analysis is applied to each deep feature set, extracted from each network. Such feature sets are then used to train an SVM classifier individually. Afterward, a selected number of principal components from each deep feature set are fused and compared with the fusion of the deep features extracted from each CNN. The results show that the proposed system is effective and capable of detecting COVID-19 and distinguishing it from non-COVID-19 cases with an accuracy of 94.7%, AUC of 0.98 (98%), sensitivity 95.6%, and specificity of 93.7%. Moreover, the results show that the system is efficient, as fusing a selected number of principal components has reduced the computational cost of the final model by almost 32%.

Breast cancer is the most common cancer type worldwide. In cancer studies, histopathological breast images are used in the process of diagnosis. In this paper, we defined three sets of features to represent the characteristics of the cell nuclei to detect malignant cases. Geometric, directional, and intensity-based features, a total of 33, are derived and evaluated using breast cancer histopathological images from the BreaKHis database. Four machine learning algorithms, including Decision Tree, Support Vector Machines, K-Nearest Neighbor, and Narrow Neural Networks (NNN), are designed to assess the efficiency of the sets. The preliminary results showed that the proposed methodology achieved high performance in classifying cancerous cells as the directional feature set was the most effective set among the three sets. The combination of the sets achieved the best performance by the NNN, which reached an accuracy, recall, precision, AUC, and F1 score of 96.9%, 97.4%, 98%, 98.8%, and 97.7%, respectively.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

Medical images classification is a challenging research topic in the field of computer vision, especially when applied to diagnosis of breast cancer (BC). Nowadays, histopathological image is marked as the gold standard for diagnosing BC. However, such diagnosis is heavily dependent on the clinician's experience, which is extremely time consuming and is subjected to human error even for experienced doctors. To address those problems, this paper implements an automated method for distinguishing the benign from the malignant tumor based on a convolutional neural network (CNN). Traditional deep CNN and machine learning methods not only lead to poor performance, but also fail to make full use of the long-term dependence between some key features and image tags. To further meet the high accuracy requirement of diagnosis, according to the characteristics of histopathological images, we propose a novel CNN framework. Firstly, a normal image is augmented to solve the problem about having a limited database. Secondly, we introduce transfer learning to obtain more accurate weight parameters that were pre-trained on the ImageNet. Thirdly, we combine various features extracted by many individual models to obtain comprehensive features. Finally, random forest is introduced to enforce classification. The experimental results show that novel CNN frameworks have better performance compared with individual models, including DenseNet and ResNet. Experimental results are able to prove the effectiveness of our strategy.

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

Breast cancer is one of the most common cancers occurring in women and is the main cause of mortality among women aged 20 to 59. Detection of breast cancer in the early stage increases survival rate to 80%, and the rate decreases to around 40% if diagnosed in its later stage. There exists significant need to diagnose breast cancer in its early stage itself among women suspected with the disease. The hematoxylin and eosin (H&E)–stained histopathology tissue samples taken by pathologists from suspected breast areas are viewed with whole slide imaging (WSI) and can be stored as digital histopathology images. Manual analysis of histopathology images is time-consuming and leads to inter- and intraobserver variability. Hence, automated classification methods are in research focus. Breast tumors are classified as benign and malignant tumors, which are non-cancerous and cancerous tumors, respectively. Currently, deep learning through convolutional neural networks is an emerging technology and has proven to be successful for image segmentation and classification applications. These deep CNNs require large labeled datasets, which are lacking in the field of medicine, especially for breast cancer images. Also, high computational power, time, and memory are required if CNN is to be trained from scratch. To overcome this, transfer learning from natural images like ImageNet, comprising of 1.2 million natural images belonging to 1,000 classes, is used. Pretrained models like VGG16, ResNet50, Inception, AlexNet, and DenseNet are employed for transfer learning, and fine-tuning is to be performed for refinement. In the proposed method, four different network architectures, namely, AlexNet, VGG16, Inception v3, and DenseNet121, are used for classification of histopathological images using transfer learning. The top dense layers of these architectures are removed, and custom dense layers are added for classification. Also, the weights of the last two layers are fine-tuned with the histopathology input images dataset. The publicly available breast cancer hisopathology images dataset BreakHis is used for validation of the proposed method. The experimental results prove that transfer learning–based CNN gives better classification accuracy compared to networks learned from scratch. Also, it is inferred from the experimental results that the pretrained model of DenseNet121 gives the highest classification accuracy of 0.9520 compared to the other models and state-of-the-art methods.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.