Brain Tumor Detection Using CNN at InsightSol Technologies In this project, I developed a Convolutional Neural Network (CNN) to detect brain tumors from MRI scan images. Leveraging the power of deep learning, I trained the model to accurately classify whether an MRI scan indicates the presence or absence of a tumor. I'm proud to announce my model achieved exceptional accuracy, showcasing the effectiveness of the implemented techniques. Through meticulous data preprocessing, model architecture design, and rigorous training, I achieved outstanding results, paving the way for more reliable diagnostic tools in healthcare. #DeepLearning #HealthcareAI #ArtificialIntelligence #CNN #MedicalImaging #InsightSolTechnologies #DataScience #MachineLearning #AI #BrainTumorDetection
Muhammad Bou Ali Nizami’s Post
More Relevant Posts
-
🚀 Excited to share that I have successfully completed a project on Brain Tumor MRI Classification! 🧠✨ Using a Convolutional Neural Network (CNN), I developed a model to classify brain tumor MRI images with high accuracy. This project involved extensive data preprocessing, model training, and evaluation, showcasing the power of machine learning in medical image analysis. This project has been a significant learning experience, enhancing my skills in deep learning, image processing, and medical applications of AI. I look forward to applying this knowledge to future projects and contributing to advancements in healthcare technology. Check out the project repository for more details. #MachineLearning #DeepLearning #AI #Healthcare #BrainTumor #MRI #DataScience #MedicalImaging #ProjectCompletion #TechInnovation
To view or add a comment, sign in
-
The code for my latest research work, entitled "A Deep Learning Approach for Medical Image Segmentation Integrating Magnetic Resonance Imaging to Enhance Brain Tumor Recognition," is now publicly available. Code: https://lnkd.in/dAkcb9ci Paper: https://lnkd.in/dKeH3uwy In this work, I leverage deep learning model to significantly improve the accuracy of brain tumor recognition through enhanced medical image segmentation. My approach integrates Magnetic Resonance Imaging (MRI) data, providing a robust and reliable solution for the medical community. This development holds immense potential for aiding in early diagnosis and improving patient outcomes. #DeepLearning #MedicalImaging #BrainTumorRecognition #MRI #AI #HealthcareInnovation #Resear
To view or add a comment, sign in
-
This project aimed to develop an AI system capable of analyzing medical images, such as X-rays, MRIs, and CT scans, to detect and diagnose various types of cancer. The system was designed to assist radiologists and medical professionals in identifying cancerous lesions with high accuracy and efficiency. By leveraging advanced convolutional neural networks (CNN) and transfer learning, the AI system sought to enhance diagnostic capabilities, reduce human error, and streamline the workflow in medical imaging departments. https://lnkd.in/djcc4Tbf
To view or add a comment, sign in
-
Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model - Scientific Reports
Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model - Scientific Reports
nature.com
To view or add a comment, sign in
-
🚀 AI in Brain Tumor Detection: Leveraging GNNs and Deep Learning 🧠 Recent advancements in AI, particularly Graph Neural Networks (GNNs) and Deep Learning, are revolutionizing brain tumor detection from medical images. Here’s how: Enhanced Image Scanning: GNNs effectively model complex brain structures, improving image segmentation and tumor localization. Accurate Diagnosis: Integrating GNNs with deep learning boosts precision, reducing false positives and improving patient outcomes. Latest Research: Recent studies highlight significant improvements in identifying tumor boundaries and classifying tumor types. Explore the latest research that’s pushing the boundaries of medical imaging and transforming brain tumor diagnosis. 📊📝 #AI #GNN #DeepLearning #BrainTumor #MedicalImaging #HealthcareInnovation
To view or add a comment, sign in
-
🚨Exciting News!🚨 I am thrilled to share my latest project on detecting and generating brain tumor-scanned MRI images using deep learning!🧠🔬 🔍Using state-of-the-art models like RESNET50, InceptionV3, EfficientNet, and VGG16, I have developed a system that accurately identifies brain tumours in MRI scans with high precision and accuracy. 🔬But that's not all! With the power of Generative Adversarial Networks (GANs), I have also created a model capable of generating synthetic MRI images of brain tumors. This opens up new possibilities for data augmentation and further research in the field. 🚀This project, available on GitHub, represents my attempt to intersect healthcare with cutting-edge AI technology. I am thrilled to contribute to advancements in medical imaging and potentially improve diagnosis and treatment outcomes for patients worldwide. 🔗Check out the project on GitHub: https://lnkd.in/gZVybDFr Let's revolutionize healthcare together!💪🌟 #AI #DeepLearning #HealthTech #MedicalImaging #DataScience #GitHub
GitHub - VasudhaSingh22/BrainTumorDetection: This project utilizes deep learning models including RESNET50, InceptionV3, EfficientNet, and VGG16 to detect brain tumors in MRI scans, while also generating synthetic MRI images using a DCGAN.
github.com
To view or add a comment, sign in
-
🚀 Achieving 93% Accuracy in Brain Tumor Classification Using CNN 🧠 I am thrilled to share my latest project: a Brain Tumor Classification System powered by Convolutional Neural Networks (CNNs)! This project focuses on accurately classifying brain MRI images into one of the following categories: Pituitary Tumor No Tumor Meningioma Glioma 🌟 Highlights of the Project: 1️⃣ Data Exploration & Visualization: I visualized the first image from each category to get a better idea of the dataset and its distinguishing features. 2️⃣ Preprocessing: Rescaled the pixel values of images for normalization. Augmented data to enhance generalization. Split the dataset into training and testing sets for robust evaluation. 3️⃣ Model Construction: Built a CNN model with carefully tuned layers for the effective capturing of image features. Optimized for high accuracy while avoiding overfitting. 4️⃣ Performance Evaluation: 93% accuracy on unseen test data. Visualized training history to track model performance. Analyzed loss and accuracy metrics for both training and testing datasets to ensure that the model is performing well on both. 💡 Key Takeaway: This project has shown the great potential of deep learning in medical image analysis for the effective and accurate diagnosis of brain tumors, which can make a big difference in treatment outcomes and patient care. 📊 Check out the results, visualizations, and detailed methodology here: https://lnkd.in/eTkjUX86 Look forward to listening to your thoughts and any feedback! #DeepLearning #HealthcareAI #CNN #BrainTumorClassification #DataScience #MedicalImaging
To view or add a comment, sign in
-
From an idea to publication! I’m happy to announce that our research paper has been published in the Journal of Intelligent Systems! Our paper, "RGB-to-Hyperspectral Conversion for Accessible Melanoma Detection: A CNN-Based Approach," introduces a method to convert standard RGB images into hyperspectral ones using convolutional neural networks. This innovation aims to make melanoma detection more accessible and practical by leveraging widely available imaging technology. Check out the full paper here: https://lnkd.in/dp2NrDef #Journal_of_Intelligent_Systems #Hyperspectral #CNN #Research #AI
To view or add a comment, sign in
-
hey there, links The news that my research paper has been selected for the 2nd International Conference on Challenges in Information Communication and Computing Technology (ICCICCT-2024) makes me very happy. My research paper is on the integrated technique of fcm and convolutional neural network for brain tumor identification. #research #tumordetection #iccicct #machinelearning #deeplearning #ai #neuralnetwork
To view or add a comment, sign in
-
See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons https://buff.ly/459QKQ1
See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons
journals.plos.org
To view or add a comment, sign in