Tag: classification

  • Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    The Fashion MNIST Dataset Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes: Each image pixel has a single pixel-value associated with it, indicating the lightness or darkness…

  • AI-Based ECG Recognition – EOY ’22 Status

    AI-Based ECG Recognition – EOY ’22 Status

    Featured Photo by cottonbro studio on pexels. Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. The recent study demonstrates that an AI is capable of automatically diagnosing the abnormalities indicated by an ECG. In this post we will review and illustrate how AI applies to ECG analysis to outperform traditional ECG analysis.…

  • 99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow 2.11.0

    99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow 2.11.0

    Workflow The entire workflow is as follows: Prerequisites We need to install the following libraries: !pip install –user tensorflow BC Dataset In this study, we use the BC Wisconsin (Diagnostic) Dataset to predict whether the BC is benign or malignant. Model features are computed from a digitized image of a fine needle aspirate (FNA) of…

  • The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    Contents: BC Dataset Conventionally, the Breast Cancer Wisconsin (Diagnostic) Data Set has been used to predict whether the breast cancer is benign or malignant. Features were computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The dataset can…

  • DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    Featured Photo by Luan Rezende Automatic detection and alarm of abnormal electrocardiogram (ECG aka EKG) events play an important role in an ECG monitor system; however, popular classification models based on standard supervised ML fail to detect abnormal ECG accurately. In this project, we implement an ECG anomaly detection framework based on the recently proposed…

  • ML/AI Breast Cancer Diagnosis with 98% Confidence

    ML/AI Breast Cancer Diagnosis with 98% Confidence

    We demonstrate the importance of hyperparameter optimization (HPO) for enhancing ML prediction accuracy. Specifically, we will focus on the Random Forest Classifier (RFC) as an ensemble of decision trees. RFC is a supervised ML algorithm that has been applied successfully to the BC binary classification. 

  • Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)

    Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)

    Breast Cancer (BC) is the leading cause of death among women worldwide. The present study optimizes the use of supervised Machine Learning (ML) algorithms for detecting, analyzing, and classifying BC. We compare Logistic Regression (LR) against Gradient Boosting (GB) Classifier within the Hyperparameter Optimization (HPO) loop given by GridSearchCV. We use the publicly available BC dataset…

  • A Comparative Analysis of Breast Cancer ML/AI Binary Classifications

    A Comparative Analysis of Breast Cancer ML/AI Binary Classifications

    This study is dedicated to #BreastCancerAwarenessMonth2022 #breastcancer #BreastCancerDay @Breastcancerorg @BCAction @BCRFcure @NBCF @LivingBeyondBC @breastcancer @TheBreastCancer @thepinkribbon @BreastCancerNow. One of the most common cancer types is breast cancer (BC), and early diagnosis is the most important thing in its treatment. Recent studies have shown that BC can be accurately predicted and diagnosed using machine learning (ML) technology. Our…

  • HealthTech ML/AI Q3 ’22 Round-Up

    HealthTech ML/AI Q3 ’22 Round-Up

    Featured Photo by Andy Kelly on Unsplash This blog presents a Q3 ’22 summary of current healthtech ML/AI innovation methods, trends and challenges. Virtual reality, artificial intelligence, augmented reality, and machine learning are all healthcare technology trends that are going to play a vital role across the entire healthcare system. Let’s take a look at…

  • AI-Powered Stroke Prediction

    Contents: Introduction Stroke, also known as brain attack, happens when blood flow to the brain is blocked, preventing it from getting oxygen and nutrients from it and causing the death of brain cells within minutes. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally after ischemic heart disease,…

  • E-Commerce ML/AI Classification

    Contents: Introduction Let us look at the most popular supervised ML/AI use-case – classification of various clothing images to categorize clothing into several categories. The classification of fashion items in a photograph includes the identification of individual garments. The same has applications in social networking (Instagram, YouTube, Twitter, etc.), e-commerce (e.g. Shopify), and criminal law as…

  • Supervised ML/AI Breast Cancer Diagnostics (BCD) – The Power of HealthTech

    Pilots Related to HealthTech Infographic These plots illustrate the most basic application of ML/AI in BCD as the binary classification problem. Classification usually refers to any kind of problem where a specific type of class label is the result to be predicted from the given input field of data. This is a task which assigns a label value…

  • Python Use-Case Supervised ML/AI in Breast Cancer (BC) Classification

      https://www.canva.com/design/DAE7oU6O6QQ/share/preview?token=xH-OB2oXeQSrennmqMC2hw&role=EDITOR&utm_content=DAE7oU6O6QQ&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton Acknowledgements with the ML/AI contribution https://hiscidatmlai.blogspot.com/2022/02/digital-transformation-all-way.html… and @VismeApp #Graphics via ref https://visme.co/?ref=al24 Thanks to Mugdha Paithankar [1] and https://kaggle.com/uciml/breast-cancer-wisconsin-data… [2] for the shared open-source content! Introduction Breast Cancer (BC) continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in…