Breast cancer segmentation and classification in ultrasound images using convolutional neural network

Breast most cancers is one of the main reasons of mortality in ladies throughout the world. Early detection contributes to a discount withinside the quantity of untimely fatalities. Using ultrasound (US) pics, we gift deep studying (DL) strategies for breast most cancers segmentation and category into 3 classes: regular, benign, and malignant. The versions in most cancers length and traits are the mission of segmentation and category tasks. The proposed technique became evolved and evaluated the use of US pics amassed from 780 breast cancers. This has a look at tested using deep studying to scientific pics of breast most cancers acquired with the aid of using ultrasound scan. For evaluation, we used intersection over union (IoU), accuracy. When evaluated with IoU the nice proposed technique yielded 100%curacy on regular breast segmentation, 79.27% on benign, and 93.73% on malignant most cancers. Also, the accuracy of category three classes is 87.86%. Our have a look at indicates the usefulness of deep studying techniques for breast most cancers segmentation and category. You can locate the preskilled weights and elements of our Implementation and the prediction of our technique may be located at https://github.com/shb8086/Cancer.

Multicriteria Evaluation of Deep Neural Networks for Semantic Segmentation of Mammographies

Breast segmentation plays a vital role in the automatic analysis of mammograms. Accurate segmentation of the breast region increments the probability of a correct diagnostic and minimizes computational cost. Traditionally, model-based approaches dominated the landscape for breast segmentation, but recent studies seem to benefit from using robust deep learning models for this task. In this work, we present an extensive evaluation of deep learning architectures for semantic segmentation of mammograms, including segmentation metrics, memory requirements, and average inference time. We used several combinations of two-stage segmentation architectures composed of a feature extraction net (VGG16 and ResNet50) and a segmentation net (FCN-8, U-Net, and PSPNet). The training examples were taken from the mini Mammographic Image Analysis Society (MIAS) database. Experimental results using the mini-MIAS database show that the best net scored a Dice similarity coefficient of 99.37% for breast boundary segmentation and 95.45% for pectoral muscle segmentation.

A Multi-Scale Information Fusion Level Set for Breast Tumor Segmentation

Purpose: Mammography is considered an effective method of examination in early breast cancer screening. Massive work by distinguished researchers of breast segmentation has been proposed. However, due to the blurry boundaries of the breast tumor, the variability of its shape and the overlap with surrounding tissue, the breast tumor’s accurate segmentation still is a challenge. Methods: In this paper, we proposed a novel level set model which based on the optimized local region driven gradient enhanced level set model (OLR-GCV) to segment tumor within a region of interest (ROI) in a mammogram. Firstly, Noise, labels and artifacts are removed from breast images. The ROI is then obtained using the intuitionistic fuzzy C-means method. Finally we used OLR-GCV method to accurately segment the breast tumor. The OLR-GCV model combines regional information, enhanced edge information and optimized Laplacian of Gaussian (LOG) energy term. The regional and enhanced edge information are used to capture local, global and gradient information of breast images. The optimized Laplacian of Gaussian (LOG) energy term is introduced in the energy functional to further optimize edge information to improve segmentation accuracy. Results: We evaluated our method on the MIAS and DDSM datasets. It yielded a Dice value of 96.86% on the former and 95.51% on the latter. Our method proposed achieves higher accuracy of segmentation than other State-of-the-art Methods. Conclusions: Our method has better segmentation performance, and can be used in clinical practice.

Automatic Pectoral Muscle Removal and Microcalcification Localization in Digital Mammograms

Objectives: Breast cancer is the most common cancer diagnosed in women, and microcalcification (MCC) clusters act as an early indicator. Thus, the detection of MCCs plays an important role in diagnosing breast cancer.Methods: This paper presents a methodology for mammogram preprocessing and MCC detection. The preprocessing method employs automatic artefact deletion and pectoral muscle removal based on region-growing segmentation and polynomial contour fitting. The MCC detection method uses a convolutional neural network for region-of-interest (ROI) classification, along with morphological operations and wavelet reconstruction to reduce false positives (FPs).Results: The methodology was evaluated using the mini-MIAS and UTP datasets in terms of segmentation accuracy in the preprocessing phase, as well as sensitivity and the mean FP rate per image in the MCC detection phase. With the mini-MIAS dataset, the proposed methods achieved accuracy scores of 99% for breast segmentation and 95% for pectoral segmentation, a sensitivity score of 82% for MCC detection, and an FP rate per image of 3.27. With the UTP dataset, the methods achieved accuracy scores of 97% for breast segmentation and 91% for pectoral segmentation, a sensitivity score of 78% for MCC detection, and an FP rate per image of 0.74.Conclusions: The proposed preprocessing method outperformed the state-of-the-art methods for breast segmentation and achieved relatively good results for pectoral muscle removal. Furthermore, the MCC detection module achieved the highest test accuracy in identifying potential ROIs with MCCs compared to other methods.

Biomedical Image Segmentation and Analysis in Deep Learning

Biomedical Image Processing, such as human organ segmentation and disease analysis, is a modern field in medicine development and patient treatment. Besides there are many kinds of image formats, the diversity and complexity of biomedical data is still a big issue to all of researchers in their applications. In order to deal with the problem, deep learning give us a successful and effective solutions. Unet and LSTM are two general approaches to the most of case of medical image data. While Unet helps to teach a machine in learning data from each image accompanied with its labelled information, LSTM helps to remember states from many slices of images by times. Unet gives us the segmentation of tumor, abnormal things from biomedical images and then the LSTM gives us the effective diagnosis on a patient disease. In this paper, we show some scenarios of using Unets and LSTM to segment and analysis on many kinds of human organ images and results of brain, retinal, skin, lung and breast segmentation.

Segmentation of Medical Image Based on Superpixel Boundary Perceptual Convolutional Network in Cancer Diagnosis and Treatment

In order to reduce the computational complexity of breast tumor segmentation algorithms and improve the accuracy of breast segmentation, this paper proposes a breast tumor segmentation method based on super pixel boundary perceptual convolutional network. This method first uses super pixel segmentation convolutional network algorithm to segment breast medical images, and then uses region growth algorithm to achieve breast tumor segmentation at super pixel level. The research results show that in the classification of breast tumors, the fusion efficiency based on the classifier level is better than the fusion based on the feature set; the index R proposed and adopted in this paper can effectively select the appropriate individual classifier and generate a better performing integration 06%. Classifier, the accuracy of this classifier is 88.73%, the sensitivity is 97.06%. The method can be used to assist doctors in breast cancer diagnosis, improve the efficiency and accuracy of doctors' work diagnosis, and has certain significance for clinical research and large-scale screening of breast cancer.

Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI

Breast segmentation and mass detection in medical images are important for diagnosis and treatment follow-up. Automation of these challenging tasks can assist radiologists by reducing the high manual workload of breast cancer analysis. In this paper, deep convolutional neural networks (DCNN) were employed for breast segmentation and mass detection in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). First, the region of the breasts was segmented from the remaining body parts by building a fully convolutional neural network based on U-Net++. Using the method of deep learning to extract the target area can help to reduce the interference external to the breast. Second, a faster region with convolutional neural network (Faster RCNN) was used for mass detection on segmented breast images. The dataset of DCE-MRI used in this study was obtained from 75 patients, and a 5-fold cross validation method was adopted. The statistical analysis of breast region segmentation was carried out by computing the Dice similarity coefficient (DSC), Jaccard coefficient, and segmentation sensitivity. For validation of breast mass detection, the sensitivity with the number of false positives per case was computed and analyzed. The Dice and Jaccard coefficients and the segmentation sensitivity value for breast region segmentation were 0.951, 0.908, and 0.948, respectively, which were better than those of the original U-Net algorithm, and the average sensitivity for mass detection achieved 0.874 with 3.4 false positives per case.