In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each year.Earlier Diagnosis of the people who are at higher risk of CVDs help...In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each year.Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths.It becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm(DNN–BBoA)is proposed.The BBoA is incorporated to select the best features.The optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time complexity.The usage of a deep neural network further helps to improve the prediction accu-racy with minimal complexity.The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle Repository.The proposed work is compared with different machine learning classifiers such as Support Vector Machine,Random Forest,and Decision Tree Classifier.The accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.展开更多
In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation tec...In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm components.This is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous methods.To address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering techniques.These methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise segmentation.Additionally,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency information.This allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of classification.For classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted features.Our enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic leukaemia.Our innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved classification.Through extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification accuracy.The segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),N.myelocyte(97%),N.promyelocyte(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.展开更多
文摘In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each year.Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths.It becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm(DNN–BBoA)is proposed.The BBoA is incorporated to select the best features.The optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time complexity.The usage of a deep neural network further helps to improve the prediction accu-racy with minimal complexity.The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle Repository.The proposed work is compared with different machine learning classifiers such as Support Vector Machine,Random Forest,and Decision Tree Classifier.The accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.
文摘In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm components.This is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous methods.To address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering techniques.These methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise segmentation.Additionally,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency information.This allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of classification.For classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted features.Our enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic leukaemia.Our innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved classification.Through extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification accuracy.The segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),N.myelocyte(97%),N.promyelocyte(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.
