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

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Computational Intelligence and Neuroscience

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNe...

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

Applied Sciences, 2021

Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and ...

Breast cancer detection in mammograms using convolutional neural network

2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018

Breast cancer is among world's second most occurring cancer in all types of cancer. Most common cancer among women worldwide is breast cancer. There is always need of advancement when it comes to medical imaging. Early detection of cancer followed by the proper treatment can reduce the risk of deaths. Machine learning can help medical professionals to diagnose the disease with more accuracy. Where deep learning or neural networks is one of the techniques which can be used for the classification of normal and abnormal breast detection. CNN can be used for this detection. Mammograms-MIAS dataset is used for this purpose, having 322 mammograms in which almost 189 images are of normal and 133 are of abnormal breasts. Promising experimental results have been obtained which depict the efficacy of deep learning for breast cancer detection in mammogram images and further encourage the use of deep learning based modern feature extraction and classification methods in various medical imaging applications especially in breast cancer detection. It is an ongoing research and further developments are being made by optimizing the CNN architecture and also employing pre-trained networks which will hopefully lead to higher accuracy measures. Proper segmentation is mandatory for efficient feature extraction and classification.

Applying deep learning methods for mammography analysis and breast cancer detection

Applied Sciences, 2023

Breast cancer is a serious medical condition that requires early detection for successful treatment. Mammography is a commonly used imaging technique for breast cancer screening, but its analysis can be time-consuming and subjective. This study explores the use of deep learning-based methods for mammogram analysis, with a focus on improving the performance of the analysis process. The study is focused on applying different computer vision models, with both CNN and ViT architectures, on a publicly available dataset. The innovative approach is represented by the data augmentation technique based on synthetic images, which are generated to improve the performance of the models. The results of the study demonstrate the importance of data pre-processing and augmentation techniques for achieving high classification performance. Additionally, the study utilizes explainable AI techniques, such as class activation maps and centered bounding boxes, to better understand the models’ decision-making process.

Classification of Breast Cancer from Digital Mammography Using Deep Learning

Inteligencia Artificial, 2020

Breast cancer is the most frequent in females. Mammography has proven to be the most effective method for the early detection of this type of cancer. Mammographic images are sometimes difficult to understand, due to the nature of the anomalies, the low contrast image and the composition of the mammary tissues, as well as various technological factors such as spatial resolution of the image or noise. Computer-aided diagnostic systems have been developed to increase the accuracy of mammographic examinations and be used by physicians as a second opinion in obtaining the final diagnosis, and thus reduce human errors. Convolutional neural networks are a current trend in computer vision tasks, due to the great performance they have achieved. The present investigation was based on this type of networks to classify into three classes, normal, benign and malignant tumour. Due to the fact that the miniMIAS database used has a low number of images, the transfer learning technique was applied t...

Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

2021

In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate breast masses in mammograms. Next we apply transfer learning to the classifier of the SVM. Four techniques are devised for the experimental evaluation of the proposed system. The fourth technique combines the Deep-CNN with texture features and local features extracted by the scale-invariant feature transform (SIFT) algorithm. Experiments are designed to measure the performance of the various techniques. The results demonstrate that the proposed CNN coupled with the texture features and the SIFT outperforms the other models and performs best with transfer learning embedded. The accuracy of this model is 97.8%, with a true positive rate of 98.45% and a true negative rate of 96%.

A Novel Transfer-Learning Model for Automatic Detection and Classification of Breast Cancer Based Deep CNN

2021

Breast cancer (BC) is a leading cause of cancer death among women in which breast cells develop out of control is by encouraging patients to receive timely care, early detection of BC increases the likelihood of survival. In this context, a new deep learning (DL) model is presented for automatic detection and classification of the suspected area of the breast based on the transfer learning (TL) technique. A pre-trained visual geometry group (VGG)-19, VGG16, and InceptionV3 networks are used in the presented model to transfer their learning parameters for improving the performance of breast tumor classification. The main goals of this project are to use segmentation to automatically determine the affected breast tumor region, reduce training time, and improve classification performance. In the presented model, the Mammographic Image Analysis Society (MIAS) dataset is used for extracting the breast tumor features. We have chosen four evaluation metrics for evaluating the performance o...

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms

Computer Methods and Programs in Biomedicine, 2020

Background and Objective: Deep learning detection and classification from medical imagery are key components for computer-aided diagnosis (CAD) systems to efficiently support physicians leading to an accurate diagnosis of breast lesions. Methods: In this study, an integrated CAD system of deep learning detection and classification is proposed aiming to improve the diagnostic performance of breast lesions. First, a deep learning YOLO detector is adopted and evaluated for breast lesion detection from entire mammograms. Then, three deep learning classifiers, namely regular feedforward CNN, ResNet-50, and InceptionResNet-V2, are modified and evaluated for breast lesion classification. The proposed deep learning system is evaluated over 5fold cross-validation tests using two different and widely used databases of digital X-ray mammograms: DDSM and INbreast. Results: The evaluation results of breast lesion detection show the capability of the YOLO detector to achieve overall detection accuracies of 99.17% and 97.27% and F1-scores of 99.28% and 98.02% for DDSM and INbreast datasets, respectively. Meanwhile, the YOLO detector could predict 71 frames per second (FPS) at the testing time for both DDSM and INbreast datasets. Using detected breast lesions, the classification models of CNN, ResNet-50, and InceptionResNet-V2 achieve promising average overall accuracies of 94.50%, 95.83%, and 97.50%, respectively, for the DDSM dataset and 88.74%, 92.55%, and 95.32%, respectively, for the INbreast dataset. Conclusion: The capability of the YOLO detector boosted the classification models to achieve a promising breast lesion diagnostic performance. Such prediction results should help to develop a feasible CAD system for practical breast cancer diagnosis.

Mammogram Classification and Segmentation Through Deep Learning

2021

Given the extreme importance in early detection of breast cancer, a compelling search for Computer-Aided Detection (CAD) techniques drove Deep Learning (DL) researchers to investigate potential mammography screening applications. This work proposes the use of sophisticated Convolutional Neural Networks (CNNs) for classification and segmentation of mass and Micro-Calcification (MC) lesions on the publicly available INbreast dataset. Using an Attention Dense U-Net model, lesion segmentations are extracted with a Dice Coefficient of (0.71 ± 0.08) for masses and (0.58 ± 0.05) for MCs. As a result, classification using a Dense Multi-View model incorporating these predicted lesion segmentations shows competitive results regarding the State of the Art in fully-automated classification of breast screening exams (Normal, Benign, Malignant), achieving a 3-Class Mean AUC of (0.79 ± 0.06).

Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study

Journal of Imaging

Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch.