Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced ima...Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.展开更多
Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social m...Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social media.In literature,it has been reported that SA data is created for English language in excess of any other language.It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language.An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.Neural word embedding has been employed in literature,since it is less labor-intensive than automatic feature engineering.By ignoring the context of sentiment,most of the word-embedding models follow syntactic data of words.The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)model.The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language.At first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful format.In addition,TF-IDF model is exploited as a feature extractor to generate the feature vectors.Besides,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of sentiments.At last,the hyperparameters of ABLSTM model are optimized using DFO algorithm.The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects.The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.展开更多
World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views...World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,etc.Sentiment Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and neutral.However,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So,there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this,SOADL-SAC model carries out data preprocessing to clean the input data.In addition,Glove technique is applied to generate the feature vectors.Moreover,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the sentiments.Finally,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.展开更多
Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
基金funded by Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/MRC/13/771-4.
文摘Breast cancer remains one of the most pressing global health concerns,and early detection plays a crucial role in improving survival rates.Integrating digital mammography with computational techniques and advanced image processing has significantly enhanced the ability to identify abnormalities.However,existing methodologies face persistent challenges,including low image contrast,noise interference,and inaccuracies in segmenting regions of interest.To address these limitations,this study introduces a novel computational framework for analyzing mammographic images,evaluated using the Mammographic Image Analysis Society(MIAS)dataset comprising 322 samples.The proposed methodology follows a structured three-stage approach.Initially,mammographic scans are classified using the Breast Imaging Reporting and Data System(BI-RADS),ensuring systematic and standardized image analysis.Next,the pectoral muscle,which can interfere with accurate segmentation,is effectively removed to refine the region of interest(ROI).The final stage involves an advanced image pre-processing module utilizing Independent Component Analysis(ICA)to enhance contrast,suppress noise,and improve image clarity.Following these enhancements,a robust segmentation technique is employed to delineated abnormal regions.Experimental results validate the efficiency of the proposed framework,demonstrating a significant improvement in the Effective Measure of Enhancement(EME)and a 3 dB increase in Peak Signal-to-Noise Ratio(PSNR),indicating superior image quality.The model also achieves an accuracy of approximately 97%,surpassing contemporary techniques evaluated on the MIAS dataset.Furthermore,its ability to process mammograms across all BI-RADS categories highlights its adaptability and reliability for clinical applications.This study presents an advanced and dependable computational framework for mammographic image analysis,effectively addressing critical challenges in noise reduction,contrast enhancement,and segmentation precision.The proposed approach lays the groundwork for seamless integration into computer-aided diagnostic(CAD)systems,with the potential to significantly enhance early breast cancer detection and contribute to improved patient outcomes.
基金The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the National Research Priorities funding program,support under code number:NU/NRP/SERC/11/3.
文摘Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social media.In literature,it has been reported that SA data is created for English language in excess of any other language.It is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic language.An earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the text.Neural word embedding has been employed in literature,since it is less labor-intensive than automatic feature engineering.By ignoring the context of sentiment,most of the word-embedding models follow syntactic data of words.The current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)model.The aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic language.At first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful format.In addition,TF-IDF model is exploited as a feature extractor to generate the feature vectors.Besides,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of sentiments.At last,the hyperparameters of ABLSTM model are optimized using DFO algorithm.The performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different aspects.The experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4350139DSR01).
文摘World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,etc.Sentiment Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and neutral.However,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of applications.So,there is a need exists to develop a proper technique for both identification and classification of sentiments in social media.To get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning capability.The current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social media.The presented SOADL-SAC model involves the proper identification and classification of sentiments in social media.In order to attain this,SOADL-SAC model carries out data preprocessing to clean the input data.In addition,Glove technique is applied to generate the feature vectors.Moreover,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the sentiments.Finally,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier results.In order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark datasets.The experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
