Diagnexia’s Post

Segmentation of ovarian cyst in ultrasound images using AdaResU-net with optimization algorithm and deep learning model - Scientific Reports

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

🔥 Read our Highly Cited Paper 📚 An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection 🔗 https://lnkd.in/gnA7ym7d 👨🔬 by JiNa Lee and JiYeoun Lee 🏫 Seokyeong University #pathologicalvoice #disorderedvoice #intelligentmedical

See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons https://buff.ly/459QKQ1

🎉 I'm thrilled to share that our latest research paper titled "Efficient Approach for Kidney Stone Treatment Using Convolutional Neural Network" has been successfully published in the prestigious journal Traitement du Signal by International Information and Engineering Technology Association (IIETA). 📊 Impact Factor (JCR) 2022: 1.9 ℹ 📝 Our research addresses the critical task of kidney stone treatment, presenting a Convolutional Neural Network (CNN) based approach for the classification of Computed Tomography (CT) kidney images into four categories: Normal, Cyst, Tumor, and Stone. By leveraging deep learning techniques, our model achieves remarkable accuracy in identifying kidney abnormalities, offering promising advancements in diagnostic accuracy and patient care. 🌐 Read the full paper: DOI: https://lnkd.in/gWdQ7Bny I would like to thank Dr.Nallakaruppan kailasanathan sir for his invaluable mentorship and also my co-authors: Dr. Veena Grover, Siddhesh Fuladi, Mohamed Baza, PhD, Dr. Hani Alshahrani. #MedicalResearch #KidneyStoneTreatment #ConvolutionalNeuralNetworks #HealthcareInnovation 🚀

I am glad to share that our recent study, “Hybrid Parallel Fuzzy CNN Paradigm: Unmasking Intricacies for Accurate Brain MRI Insights,” is published by IEEE Transaction on Fuzzy Systems (Q1, top 4).  The Hybrid Parallel Fuzzy CNN (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel combination tackles problems related to brain image processing, reducing problems such as noise and hazy borders that are common in Magnetic Resonance Imaging (MRI). Unlike other CNN models, HP-FCNN combines fine-grained fuzzy representations with crisp CNN features, improving interpretability by displaying hidden layers. This insight into activation patterns facilitates comprehension of the decision-making processes necessary for the diagnosis of brain diseases. HP-FCNN outperforms other pretrained models (ResNet, DenseNet, VGG, and EfficientNet) on measures such as the confusion matrix and AUC-ROC, according to comparative assessments. DOI: 10.1109/TFUZZ.2024.3372608 https://lnkd.in/dRpEXQyC

Deep learning methods to detect Alzheimer's disease from MRI: A systematic review Mariana Coelho, Martin Cerny, João Manuel R. S. Tavares Expert Systems 42(1):e13463, January 2025 https://lnkd.in/ddEvJmPF

👉🏼 Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model 🤓 Daisuke Ono 👇🏻 https://lnkd.in/e2RK6-xr 🔍 Focus on data insights: - GPT-4Vision accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. - The interpretation of images was notably influenced by the provided textual context, sometimes leading to diagnostic inaccuracies. - Few-shot learning significantly improved GPT-4Vision's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning. 💡 Main outcomes and implications: - GPT-4Vision faces challenges in independently interpreting histopathological images. - Few-shot learning is a promising approach to enhance GPT-4Vision's performance, especially in neuropathology where acquiring extensive labeled datasets is challenging. 📚 Field significance: - Artificial intelligence advancements like GPT-4Vision have expanded the potential for medical image interpretation. - Comparative analysis with traditional convolutional neural network models provides insights into improving diagnostic accuracy in neurodegenerative disease histopathology. 🗄️: #neurodegenerative #histopathology #GPT-4Vision #fewshotlearning

🎉 I'm thrilled to share that our latest research paper titled "Efficient Approach for Kidney Stone Treatment Using Convolutional Neural Network" has been successfully published in the prestigious journal Traitement du Signal by International Information and Engineering Technology Association (IIETA). 📊 Impact Factor (JCR) 2022: 1.9 ℹ 📝 Our research addresses the critical task of kidney stone treatment, presenting a Convolutional Neural Network (CNN) based approach for the classification of Computed Tomography (CT) kidney images into four categories: Normal, Cyst, Tumor, and Stone. By leveraging deep learning techniques, our model achieves remarkable accuracy in identifying kidney abnormalities, offering promising advancements in diagnostic accuracy and patient care. 🌐 Read the full paper: DOI: https://lnkd.in/gWdQ7Bny I would like to thank my mentor and faculty, Dr.Nallakaruppan kailasanathan sir and also my co-authors: Dr. Veena Grover, Himakshi Chaturvedi, Mohamed Baza, PhD, Dr. Hani Alshahrani. #MedicalResearch #KidneyStoneTreatment #ConvolutionalNeuralNetworks #HealthcareInnovation 

Just published in Stroke: SVIN! "Do Deep Learning Algorithms Accurately Segment Intracerebral Hemorrhages on Noncontrast Computed Tomography?" I’m immensely grateful for my co-authors' efforts. Big thanks to Professor David Liebeskind for steering us through, and to Diana Zarei, MD, and Shahriar Kolahi, MD, for their pivotal roles. Our findings underscore deep learning's potential to enhance clinical neuroimaging and streamline diagnostics. Here’s to more groundbreaking work! 🧠📊 #DeepLearning #Neuroimaging #StrokeResearch #MedicalAI