Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep infor...Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.展开更多
A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs）...A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.展开更多
Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research l...Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.展开更多
Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use ...Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.展开更多
Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original mean...Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original meaning of the features after reduction. The benefits of FS are twofold：it considerably decreases the running time of the induction algorithm,and increases the accuracy of the resulting model. This paper analyses the FS process in mammogram classification using fuzzy logic and rough set theory. Rough set and fuzzy logic based Quickreduct algorithms are applied for the FS from the features extracted using gray level co-occurence matrix（GLCM） constructed over the mammogram region. The predictive accuracy of the features is tested using NaiveBayes,Ripper,C4.5,and ant-miner algorithms. The results show that the ant-miner produces significant result comparing with others and the number of features selected using fuzzy-rough quick reduct algorithm is minimum,too.展开更多
This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further imag...This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further image processing gives a gray-level histogram with distinguished characteristics, which facilitates the segmentation of the suspicious masses. The experiment results based on 25 digital sample mammograms, which are definitely diagnosed, are analyzed and evaluated briefly.展开更多
Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying...Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.展开更多
文摘Breast cancer is a deadly disease and radiologists recommend mammography to detect it at the early stages. This paper presents two types of HanmanNets using the information set concept for the derivation of deep information set features from ResNet by modifying its kernel functions to yield Type-1 HanmanNets and then AlexNet, GoogLeNet and VGG-16 by changing their feature maps to yield Type-2 HanmanNets. The two types of HanmanNets exploit the final feature maps of these architectures in the generation of deep information set features from mammograms for their classification using the Hanman Transform Classifier. In this work, the characteristics of the abnormality present in the mammograms are captured using the above network architectures that help derive the features of HanmanNets based on information set concept and their performance is compared via the classification accuracies. The highest accuracy of 100% is achieved for the multi-class classifications on the mini-MIAS database thus surpassing the results in the literature. Validation of the results is done by the expert radiologists to show their clinical relevance.
文摘A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R432),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Breast cancer is a type of cancer responsible for higher mortality rates among women.The cruelty of breast cancer always requires a promising approach for its earlier detection.In light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors.In addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram.Accordingly,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of mammograms.For this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS databases.Maximumclassification accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing models.Furthermore,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.
文摘Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process.At the same time,breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques.Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate.But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives.For resolving the issues of false positives of breast cancer diagnosis,this paper presents an automated deep learning based breast cancer diagnosis(ADL-BCD)model using digital mammograms.The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms.The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation.In addition,Deep Convolutional Neural Network based Residual Network(ResNet 34)is applied for feature extraction purposes.Specifically,a hyper parameter tuning process using chimp optimization algorithm(COA)is applied to tune the parameters involved in ResNet 34 model.The wavelet neural network(WNN)is used for the classification of digital mammograms for the detection of breast cancer.The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures.The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.
文摘Feature selection（FS） refers to the process of selecting those input attributes that are most predictive of a given outcome. Unlike other dimensionality reduction methods,feature selectors preserve the original meaning of the features after reduction. The benefits of FS are twofold：it considerably decreases the running time of the induction algorithm,and increases the accuracy of the resulting model. This paper analyses the FS process in mammogram classification using fuzzy logic and rough set theory. Rough set and fuzzy logic based Quickreduct algorithms are applied for the FS from the features extracted using gray level co-occurence matrix（GLCM） constructed over the mammogram region. The predictive accuracy of the features is tested using NaiveBayes,Ripper,C4.5,and ant-miner algorithms. The results show that the ant-miner produces significant result comparing with others and the number of features selected using fuzzy-rough quick reduct algorithm is minimum,too.
基金This research is partly supported by the National Natural Science Foundation of China! (No.69873031).
文摘This paper presents a novel approach for detection of suspicious regions in digitized mammograms. The edges of the suspicious region in mammogram are enhanced using an improved logic filter. The result of further image processing gives a gray-level histogram with distinguished characteristics, which facilitates the segmentation of the suspicious masses. The experiment results based on 25 digital sample mammograms, which are definitely diagnosed, are analyzed and evaluated briefly.
基金This project was supported by the Deanship of Scientific Research at Prince SattamBin Abdulaziz University under research Project#(PSAU-2022/01/20287).
文摘Breast Cancer(BC)is considered the most commonly scrutinized can-cer in women worldwide,affecting one in eight women in a lifetime.Mammogra-phy screening becomes one such standard method that is helpful in identifying suspicious masses’malignancy of BC at an initial level.However,the prior iden-tification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis.Deep learning(DL)techniques were broadly utilized for medical imaging applications,particularly breast mass classi-fication.The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis(CAD)systems since the learning cap-ability of Machine Learning(ML)techniques was constantly improving.This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoenco-der Breast Cancer Classification(HTDHDAE-BCC)on Digital Mammograms.The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC.In the HTDHDAE-BCC model,the initial stage of image preprocessing is carried out using an average median filter.In addition,the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors.The parameter tuning process uses the binary spider monkey opti-mization(BSMO)algorithm.The HTDHDAE-BCC model exploits chameleon swarm optimization(CSO)with the DHDAE model for BC classification.The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database.The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.
