Performance based CBR Mass detection in mammograms

Breast cancer is the second most common type of cancer in the world. Several computer-aided detection and diagnosis systems have been used to assist health experts and to indicate suspect areas that would be difficult to perceive by the human eye; this approach has aided in the detection and diagnosis of cancer. The present work proposes a method for the automatic detection of masses in digital mammograms by using quality threshold (QT), a correlogram function, and the support vector machine (SVM). This methodology comprises the following steps: The first step is to perform preprocessing with a low-pass filter, which increases the scale of the contrast, and the next step is to use an enhancement to the wavelet transform with a linear function. After the preprocessing is segmentation using QT; then, we perform post-processing, which involves the selection of the best mass candidates. This step is performed by analyzing the shape descriptors through the SVM. For the stage that involves the extraction of texture features, we used Haralick descriptors and a correlogram function. In the classification stage, the SVM was again used for training, validation, and final test. The results were as follows: sensitivity 92.31 %, specificity 82.2 %, accuracy 83.53 %, mean rate of false positives per image 1.12, and area under the receiver operating characteristic (ROC) curve 0.8033. Breast cancer is notable for presenting the highest mortality rate in addition to one of the smallest survival rates after diagnosis. An early diagnosis means a considerable increase in the survival chance of the patients. The methodology proposed herein contributes to the early diagnosis and survival rate and, thus, proves to be a useful tool for specialists who attempt to anticipate the detection of masses.

For the purpose of accurate and efficient mass detection in full-field digital mammograms, weproposeamethodfor automated mass detection that consists oftwostages: suspicious region localization and false-positive (FP) reduction, by classifying these regions into mass and non-mass regions (normal tissues). In the first stage, the density of the wavelet coefficients based on Quincunx Lifting Scheme (DWC-QLS) is used to find suspicious regions (regions of interest, ROIs) in full mammograms. In the second stage, a patch-based CNN classifier is developed as an FP reduction to classify the suspicious regions. The main aim of this stage is to reduce the false-positive suspicious regions while keeping the true-positive suspicious regions. To further improve the performance of the FP reduction, the effectiveness of different transfer learning strategies is further explored and the best fine-tuning strategy in training InceptionV3 model is determined experimentally. The experimental results show that the proposed method can achieve an overall performance of 0.98 TPR @1.43 FPI on the INbreast database. In addition, the suggested segmentation method detects the mass location with 100% sensitivity and average of 5.4 false positives per image. Based on the obtained results,the introduced method was able to successfully detectand classify suspicious regions in digital mammograms and provide better TPR and FPI results in comparison with other state-of-the-art method.

Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system

A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms

Background: Alongside traditional imaging, current development in non-invasive breast carcinoma (BC) diagnosis, such as cell-free DNA-based technologies, have demonstrated promising potential in BC diagnosis. However, achieving early detection remains a challenge. Micronuclei (MN) are extranuclear bodies containing damaged chromosome segments indicative of genomic instability. Elevated levels of MN-containing erythrocytes have been studied in genotoxicity testing and cancer diagnosis. Here, we sequenced the isolated and purified micronuclei DNA (MN-DNA) from erythrocytes in 2ml peripheral blood using a method we previously developed (WO2021/228246 A1) and identified a series of distinct features in read counts that enabled to detect early-stage BC patients. Methods: We collected a clinical study cohort of 172 BC patients and 216 non-BC controls from 1-2ml peripheral blood. MN-DNA was isolated from erythrocytes and performed whole-genome sequencing. We selected distinctive tumor-associated MN-DNA features identified by genome-wide analysis to differentiate between controls and BC for KEGG pathway enrichment and constructed a classification model based on these features. Results: Genome-wide analysis of MN-DNA revealed significant regions between controls and BC patients. Based on covered genes, we observed significant enrichment in the estrogen signaling pathway. Utilizing these distinct MN- DNA features, we constructed a classification model that achieved an AUC of 96%, with a 95% specificity and 88% sensitivity in the test cohort. The model identified early BC (stage 0-I) and advanced BC (stage II-IV) with sensitivities of 100% or 84%, respectively. Conclusions: Our findings highlight that MN-DNA, a form of cytoplasmic DNA, provides a valuable tool for the accurate detection of BC, particularly in its early stages. The MN-DNA pattern is linked to the estrogen signaling pathway, offering novel insights and mechanisms that differ from current diagnostic approaches. Research sponsor: Timing Biotech. Early Detection of Primary Cancer and Relapse Citation Format: Wenjv Mo, Xingyun Yao, Haobo Sun, Honghao Liang, Jie Jin, Xiaowen Ding, Xiaofei Gao. Utilizing breast cancer-specific micronuclei DNA features from erythrocytes for early detection of breast carcinoma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A044.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Computer aided detection system for micro calcifications in digital mammograms

Detection of suspicious masses in mammograms play a vital role in early diagnosis of breast cancer, to reduce the death rate among women. The presence of masses and calcification’s in mammograms is distinguishing signs for breast cancer diagnosis. But in some cases, due to contrast variation, fuzzy boundaries and presence of noise in mammograms, segmentation and detection of masses is challenging assignment. This paper presents a new segmentation approach to detect masses in mammographic images. The proposed approach consist of artifacts elimination and pectoral region extraction, suspicious mass enhancement using dual morphological operation technique and finally, extraction of Regions of Interest (ROIs) from background using scaled Reyni entropy. The proposed system has been tested on two data-sets i.e. mini- Mammographic Image Analysis Society (mini-MIAS) and Digital Database for Screening Mammography (DDSM), over 50 and 90 mammograms respectively. Performance achieved with proposed system in terms of True Positive Fraction (TPF) yields 93.2% and 93.9% respectively, at the rate of 1.48 and 0.74 average False Positive per Image (FP/I), tested on both Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) views. The obtained experimental results demonstrates that proposed method gives improved results for mass detection and can be useful for radiologists in diagnosis of breast cancer at early stage.

Mammography is a gold standard imaging modality and is widely used for breast cancer screening. With recent advances in the field of deep learning, the use of deep convolution neural networks (CNNs) in medical image analysis has become very encouraging. The aim of this study is to exploit CNNs for mass detection in mammograms using pre-trained networks. We use the resnet-50 CNN architecture pre-trained with the ImageNet database to perform mass detection on two publicly available image datasets: CBIS-DDSM and INbreast. We demonstrate that the CNN model pretrained using natural image database (ImageNet) can be effectively finetuned to yield better results, compared to randomly initialized models. Further, the benefit of applying transfer learning on a smaller dataset is demonstrated by using the best model obtained from CBIS-DDSM training to finetune on the INbreast database. We analyzed the adaptability of the CNN’s last fully connected (FC) layer and the all convolutional layers to detect masses. The results showed a testing accuracy of 0.92 and an area under the receiver operating characteristic curve (AUC) of 0.98 for the model finetuned on all convolutional layers, while testing accuracy of 0.86 and AUC=0.93 when the model is trained only on the last FC layer.

Recently, Digital mammogram has become one of the most effective techniques for early breast cancer detection. The aim of this study is to develop an automated system for digital mammogram analysis. In the proposed system, the regions of interest (ROIs) in the mammogram are firstly segmented by a topographic representation method called the is contour map. Subsequently, the textural, intensity and shape features are extracted from the ROIs. Then an optimal feature selection method (Correlation-based Feature Selection, CFS) is used to select some important features to classify the ROIs as either masses or non-masses. Finally, we use these selected features to train the cost-sensitive BP neural network. The experimental results show that the proposed method can produce better identification performance than some other algorithms.

Computer-aided diagnosis: Detection of masses on digital mammograms

Mammography has been one of the most reliable methods for early detection of breast carcinomas. The main objective of this paper is to detect and segment the tumor from mammogram images that helps to provide support for the clinical decision to perform biopsy of the breast. In this paper, there are two aspects to segmentation in mammography. First is to separate out the mammogram from the background and the identification of putative masses and the pectoral muscle. The extraction approach is done using basic region growing method to identify the tumor. Second is to extract the features from segmented masses and classifies the masses by case base reasoning method. The experimental results are shown in this paper till the first phase of mass segmentation.

Female breast cancer is a major cause of death in occidental countries. CAD/CADx systems can aidradiologists in detection and diagnostic of lesions in mammograms. In this work, we present a methodologyto detect masses from mammograms. The K-means clustering algorithm is used to split the mammogramsin regions. Each region is then classified through a Support Vector Machine (SVM) as mass or non-massregion. SVM is a machine-learning method, based on the principle of structural risk minimization, whichperforms well when applied to data outside the training set. We use a set of textural and shape measures todetect suspicious regions, as bening and malignant masses. Each textural measure (contrast, homogeneity,inverse difference moment, entropy and energy) is computed through the co-ocurrence matrix technique.The methodology obtained an accuracy of 93.11% discriminate mass from non-mass elements.

We are developing computer-aided diagnosis (CAD) schemes for the detection of clustered microcalcifications and masses in digital mammograms. Here, CAD refers to a diagnosis made by a radiologist who uses the computerized analyses of radiographic images as a 'second opinion'. The radiologist would make the final diagnostic decision. The aim of CAD is to improve diagnostic accuracy by reducing the number of missed diagnoses. In this preliminary evaluation, 30 clinical cases from December 1991 having a focal mammographic finding were analyzed.

Octamer 4 (OCT4) has been postulated as a cancer stem cell biomarker. This study aimed to delineate the role and the clinicopathological implications of OCT4 expression in the development, progression, and lymph nodal metastatic potential of breast carcinoma. This study was conducted on a total number of 60 breast specimens. OCT4 immunohistochemical expression was assessed in 12 benign epithelial hyperplasia cases and 48 breast carcinoma cases with their adjacent carcinoma in-situ component and their corresponding metastatic lymph nodes (MLNs). OCT4 expression in breast carcinomas was significantly higher than that in hyperplastic lesions. Moderate agreement was found between OCT4 expression in invasive carcinoma and the adjacent in-situ component. In breast carcinomas, OCT4 was significantly correlated with larger tumor size, higher tumor grade, positive lymph node metastasis, high Ki67 index, and triple-negative subtype and inversely correlated with estrogen and progesterone status. By logistic regression, positive lymph node metastasis and high Ki67 index were found to be independently associated with OCT4 expression. Moderate significant agreement was recognized between OCT4 expression in the primary breast carcinomas and their matched metastatic lymph nodes. Thus, OCT4 might serve as an important biological marker for contemplating the carcinogenesis, progression, metastatic potential, and prognosis of breast carcinoma. Moreover, it could be an emerging tool for early detection of breast carcinoma and a forthcoming candidate for predicting lymph node metastasis.