Breast cancer classification using deep learning approaches and histopathology image: a comparison study

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

Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images

Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most recent BRACS dataset of histological (H\&E) stained images was used to classify breast cancer tumours, which contains both the whole-slide images (WSI) and region-of-interest (ROI) images, however, for our study we have considered ROI images. We have experimented using different pre-trained deep learning models, such as Xception, EfficientNet, ResNet50, and InceptionResNet, pre-trained on the ImageNet weights. We pre-processed the BRACS ROI along with image augmentation, upsampling, and dataset split strategies. For the default dataset split, the best results were obtained by ResNet50 achieving 66\% f1-score. For the custom dataset split, the best results were obtained by performing upsampling and image augmentation which results in 96.2\% f1-score. Our second approach also reduced the number of false positive and false negative classifications to less than 3\% for each class. We believe that our study significantly impacts the early diagnosis and identification of breast cancer tumors and their subtypes, especially atypical and malignant tumors, thus improving patient outcomes and reducing patient mortality rates. Overall, this study has primarily focused on identifying seven (7) breast cancer tumor subtypes, and we believe that the experimental models can be fine-tuned further to generalize over previous breast cancer histology datasets as well.Comment: 11 pages, 4 figure

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Deep Learning for Classification of Brain Tumor Histopathological Images

Histopathological image classification has been at the forefront of medical research. We evaluated several deep and non-deep learning models for brain tumor histopathological image classification. The challenges were characterized by an insufficient amount of training data and identical glioma features. We employed transfer learning to tackle these challenges. We also employed some state-of-the-art non-deep learning classifiers on histogram of gradient features extracted from our images, as well as features extracted using CNN activations. Data augmentation was utilized in our study. We obtained an 82% accuracy with DenseNet-201 as our best for the deep learning models and an 83.8% accuracy with ANN for the non-deep learning classifiers. The average of the diagonals of the confusion matrices for each model was calculated as their accuracy. The performance metrics criteria in this study are our model’s precision in classifying each class and their average classification accuracy. Our result emphasizes the significance of deep learning as an invaluable tool for histopathological image studies

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements