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BLOOD CANCER CELL CLASSIFICATION USING DENSENET- Medical Image Classification Using A Light-Weighted Hybrid Neural Network Based On PCANet And DenseNet

Medical Image Classification Plays An Important Role In Disease Diagnosis Since It Can Provide Important Reference Information For Doctors. The Supervised Convolutional Neural Networks (CNNs) Such As DenseNet Provide The Versatile And Effective Method For Medical Image Classification Tasks, But They Require Large Amounts Of Data With Labels And Involve Complex And Time-consuming Training Process. The Unsupervised CNNs Such As Principal Component Analysis Network (PCANet) Need No Labels For Training But Cannot Provide Desirable Classification Accuracy. To Realize The Accurate Medical Image Classification In The Case Of A Small Training Dataset, We Have Proposed A Light-weighted Hybrid Neural Network Which Consists Of A Modified PCANet Cascaded With A Simplified DenseNet. The Modified PCANet Has Two Stages, In Which The Network Produces The Effective Feature Maps At Each Stage By Convoluting Inputs With Various Learned Kernels. The Following Simplified DenseNet With A Small Number Of Weights Will Take All Feature Maps Produced By The PCANet As Inputs And Employ The Dense Shortcut Connections To Realize Accurate Medical Image Classification. To Appreciate The Performance Of The Proposed Method, Some Experiments Have Been Done On Mammography And Osteosarcoma Histology Images. Experimental Results Show That The Proposed Hybrid Neural Network Is Easy To Train And It Outperforms Such Popular CNN Models As PCANet, ResNet And DenseNet In Terms Of Classification Accuracy, Sensitivity And Specificity.

BREAST CANCER CLASSIFICATION WITH FEATURE DATA- Breast Cancer Detection-Based Feature Optimization Using Firefly Algorithm And Ensemble Classifier

The Accurate Detection Of The Malignant Cell Of Breast Cancer, Decrease The Rate Of Mortality Of Women Around The World. The Process Of Feature Optimization, Increase The Probability Of Feature Selection And Mapping Of Data. This Paper Proposed Ensemble-based Classifier For Detection Of Breast Cancer On An Early Stage. The Proposed Ensemble Classifier Support Vector Machine Is A Base Classifier, And The Other Is Boost Classifier. The Firefly Algorithm Reduces The Variance Of Breast Cancer Features For The Selection Of Feature Components For The Classification Algorithm. The Most Dominated Work Is The Extraction Of Features Of Breast Cancer Image. For The Extraction Of Features Applied Wavelet Packet Transform, Wavelet Packet Transform Overcome The Limitation Of Wavelet Transform And Increase The Diversity Of Feature Extraction Process. The Proposed Algorithm Implemented In MATLAB Software And Tested With The Very Reputed Breast Cancer Image Dataset, MIAS And DDSM. The Proposed Algorithm’s Performance Measured With Standard Parameters Such As Accuracy, Specificity, Sensitivity, And MCC. The Evaluated Results Indicate That The Proposed Algorithm Is Better Than DWPT, SVM And BMC. The Increasing Ratio Of The Classification Algorithm Is 8% Instead Of Existing Algorithms.

HUMAN DISEASE PREDICTION USING SYMPTOMS BY MACHINE LEARNING- Symptoms Based Disease Prediction Using Machine Learning Techniques

Computer Aided Diagnosis (CAD) Is Quickly Evolving, Diverse Field Of Study In Medical Analysis. Significant Efforts Have Been Made In Recent Years To Develop Computer-aided Diagnostic Applications, As Failures In Medical Diagnosing Processes Can Result In Medical Therapies That Are Severely Deceptive. Machine Learning (ML) Is Important In Computer Aided Diagnostic Test. Object Such As Body-organs Cannot Be Identified Correctly After Using An Easy Equation. Therefore, Pattern Recognition Essentially Requires Training From Instances. In The Bio Medical Area, Pattern Detection And ML Promises To Improve The Reliability Of Disease Approach And Detection. They Also Respect The Dispassion Of The Method Of Decisions Making. ML Provides A Respectable Approach To Make Superior And Automated Algorithm For The Study Of High Dimension And Multi - Modal Bio Medicals Data. The Relative Study Of Various ML Algorithm For The Detection Of Various Disease Such As Heart Disease, Diabetes Disease Is Given In This Survey Paper. It Calls Focus On The Collection Of Algorithms And Techniques For ML Used For Disease Detection And Decision Making Processes.

COVID FOR PNEUMONIA CLASSIFICATION USING DENSENET- Deep Learning Based Detection And Segmentation Of COVID-19 - Pneumonia On Chest X-ray Image

The Outbreaks Of COVID-19 Virus Have Crossed The Limit To Our Expectation And It Breaks All Previous Records Of Virus Outbreaks. The Effect Of Corona Virus Causes A Serious Illness May Result In Death As A Consequence Of Substantial Alveolar Damage And Progressive Respiratory Failure. Automatic Detection And Classification Of This Virus From Chest X-ray Image Using Computer Vision Technology Can Be Very Useful Complement With Respect To The Less Sensitive Traditional Process Of Detecting COVID-19 I.e. Reverse Transcription Polymerase Chain Reaction (RT-PCR). This Automated Process Offers A Great Potential To Enhance The Conventional Healthcare Tactic For Tackling COVID-19 And Can Mitigate The Shortage Of Trained Physicians In Remote Communities. Again, The Segmentation Of The Infected Regions From Chest X-ray Image Can Help The Medical Specialists To View Insights Of The Affected Region. So, In This Paper We Have Used Deep Learning Based Ensemble Model For The Classification Of COVID-19, Pneumonia And Normal X-ray Image And For Segmentation We Have Used DenseNet Based U-Net Architecture To Segment The Affected Region. For Making The Ground Truth Mask Image Which Is Needed For Segmenting Purpose, We Have Used Amazon SageMaker Ground Truth Tool To Manually Crop The Activation Region (discriminative Image Regions By Which CNN Identify A Specific Class Using Grad-CAM Algorithm) Of The X-ray Image. We Have Found The Classification Accuracy 99.2% On The Available X-ray Dataset And 92% Average Accuracy From The Segmentation Process.

FALL DETECTION USING MOTION SENSOR DATA- Human Falling Detection Algorithm Based On Multisensor Data Fusion With SVM

Falling Is A Common Phenomenon In The Life Of The Elderly, And It Is Also One Of The 10 Main Causes Of Serious Health Injuries And Death Of The Elderly. In Order To Prevent Falling Of The Elderly, A Real-time Fall Prediction System Is Installed On The Wearable Intelligent Device, Which Can Timely Trigger The Alarm And Reduce The Accidental Injury Caused By Falls. At Present, Most Algorithms Based On Single-sensor Data Cannot Accurately Describe The Fall State, While The Fall Detection Algorithm Based On Multisensor Data Integration Can Improve The Sensitivity And Specificity Of Prediction. In This Study, We Design A Fall Detection System Based On Multisensor Data Fusion And Analyze The Four Stages Of Falls Using The Data Of 100 Volunteers Simulating Falls And Daily Activities. In This Paper, Data Fusion Method Is Used To Extract Three Characteristic Parameters Representing Human Body Acceleration And Posture Change, And The Effectiveness Of The Multisensor Data Fusion Algorithm Is Verified. The Sensitivity Is 96.67%, And The Specificity Is 97%. It Is Found That The Recognition Rate Is The Highest When The Training Set Contains The Largest Number Of Samples In The Training Set. Therefore, After Training The Model Based On A Large Amount Of Effective Data, Its Recognition Ability Can Be Improved, And The Prevention Of Fall Possibility Will Gradually Increase. In Order To Compare The Applicability Of Random Forest And Support Vector Machine (SVM) In The Development Of Wearable Intelligent Devices, Two Fall Posture Recognition Models Were Established, Respectively, And The Training Time And Recognition Time Of The Models Are Compared. The Results Show That SVM Is More Suitable For The Development Of Wearable Intelligent Devices.

BREAST CANCER CLASSIFICATION- Breast Cancer Detection And Classification

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

LEUKIMEA CLASSIFICATION- Detection Of Blood Cancer-Leukemia Using K-means Algorithm

Blood Cancer (Leukemia) Is One Of The Leading Causes Of Death Among Humans. The Pace Of Healing Depends Mainly On Early Detection And Diagnosis Of A Disease. The Main Reason Behind Occurrence Of Leukemia Is When Bone Marrow Produces A Lot Of Abnormal White Blood Cells This Happens. Microscopic Study On Images Is Done By Hematologists Who Make Use Of Human Blood Samples, From Which It Leads To The Requirement Of Following Methods, Which Are Microscopic Color Imaging, Image Segmentation, Clustering And Classification Which Allows Easy Identification Of Patients Suffering From This Disease. Microscopic Imaging Allows For Various Methods Of Detecting Blood Cancer In Visible And Immature White Blood Cells. Identifying Leukemia Early And Quickly Greatly Helps Practitioners In Providing Appropriate Treatment To Patients. Initially To Start With, Segmentation Stage Is Achieved By Segregating White Blood Cells From Other Blood Components I.e. Erythrocytes And Platelets By Using Statistical Parameters Such As Mean, Standard Deviation. For Diagnosing Prediction Of Leukemia, Geometrical Features Such As Area, Perimeter Of The White Blood Cell Nucleuses Investigated. In The Proposed Methodology We Make Use Of K-means, For Identifying Cancerous Stages And Its Early Detection. Experimentation And Results Were Found To Be Promising With The Accuracy Of 90% Identification Of The Cancer Cells.

ORAL CANCER CLASSIFIIACTION USING VGG16- Automated Detection And Classification Of Oral Lesions Using Deep Learning For Early Detection Of Oral Cancer

Oral Cancer Is A Major Global Health Issue Accounting For 177,384 Deaths In 2018 And It Is Most Prevalent In Low- And Middle-income Countries. Enabling Automation In The Identification Of Potentially Malignant And Malignant Lesions In The Oral Cavity Would Potentially Lead To Low-cost And Early Diagnosis Of The Disease. Building A Large Library Of Well-annotated Oral Lesions Is Key. As Part Of The MeMoSA ® (Mobile Mouth Screening Anywhere) Project, Images Are Currently In The Process Of Being Gathered From Clinical Experts From Across The World, Who Have Been Provided With An Annotation Tool To Produce Rich Labels. A Novel Strategy To Combine Bounding Box Annotations From Multiple Clinicians Is Provided In This Paper. Further To This, Deep Neural Networks Were Used To Build Automated Systems, In Which Complex Patterns Were Derived For Tackling This Difficult Task. Using The Initial Data Gathered In This Study, Two Deep Learning Based Computer Vision Approaches Were Assessed For The Automated Detection And Classification Of Oral Lesions For The Early Detection Of Oral Cancer, These Were Image Classification With ResNet-101 And Object Detection With The Faster R-CNN. Image Classification Achieved An F1 Score Of 87.07% For Identification Of Images That Contained Lesions And 78.30% For The Identification Of Images That Required Referral. Object Detection Achieved An F1 Score Of 41.18% For The Detection Of Lesions That Required Referral. Further Performances Are Reported With Respect To Classifying According To The Type Of Referral Decision. Our Initial Results Demonstrate Deep Learning Has The Potential To Tackle This Challenging Task.

EFFECTIVE HEART DISEASE PREDICTION USING HYBRID MACHINE LEARNING TECHNIQUES

Heart Disease Is One Of The Most Significant Causes Of Mortality In The World Today. Prediction Of Cardio Vascular Disease Is A Critical Challenge In The Area Of Clinical Data Analysis. Machine Learning (ML) Has Been Shown To Be Effective In Assisting In Making Decisions And Predictions From The Large Quantity Of Data Produced By The Health Care Industry. We Have Also Seen ML Techniques Being Used In Recent Developments In Different Areas Of The Internet Of Things (IoT). Various Studies Give Only A Glimpse Into Predicting Heart Disease With ML Techniques. In This Paper, We Propose A Novel Method That Aims At finding Significant Features By Applying Machine Learning Techniques Resulting In Improving The Accuracy In The Prediction Of Cardiovascular Disease. The Prediction Model Is Introduced With Different Combinations Of Features And Several Known Classification Techniques.

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