Published: SpectroNet-LSTM for Cardiac Anomaly Detection

🚨 Exciting Research Announcement! 🚨 I’m thrilled to share that our paper, "Hybrid RBC Morphology Analysis and Feature-Driven Ensemble Framework for Beta Thalassemia Diagnosis Using Convolutional Architectures, Augmentation Strategies, and SMOTE-ENN Resampling", has been accepted and published on IEEE Xplore! 📄🔬 This research presents a novel, hybrid diagnostic framework combining CNN-based red blood cell image classification and Random Forest-based analysis of hematological parameters to improve early detection of Beta Thalassemia—a pressing challenge in global healthcare. 🧠 Key Highlights: Deep learning-driven morphological analysis from blood smear images 🧬 Robust preprocessing and augmentation for real-world variability 🌍 Feature engineering + ensemble learning to boost diagnostic precision 📊 Achieved an AUC-ROC score of 0.9773, showcasing strong predictive capabilities 🎯 Proud to have collaborated with Vanshaj Awasthi Dr. Garima Bhardwaj Shukla Deepika Shekhawat Dr. Saranya A Anita Shrotriya on this interdisciplinary effort bridging machine learning, medical imaging, and clinical diagnostics. 🧾 You can read the paper here: https://lnkd.in/dGhJB6N9 #IEEE #ResearchPublication #BetaThalassemia #DeepLearning #MachineLearning #HealthcareAI #MedicalDiagnostics #CNN #EnsembleLearning #DataScience #IEEEExplore

🌟Thrilled to share about our ₹6 Lakh Funded Research Project Our research project on Sickle Cell Disease (SCD) classification using deep learning techniques for histopathological images has been published on IEEE Xplore. 📑✨ This work represents a meaningful step towards leveraging AI-driven medical imaging for better diagnosis and patient care. The project was made possible with generous funding support of ₹6 lakhs from the Institution of Eminence (IoE), University of Delhi 🎓 and in close collaboration with Balco Medical Centre, whose medical expertise added immense value. Grateful to my co-authors, collaborators, and mentors for their unwavering support throughout this journey. 🙏Dr. Arun Kumar Dubey, Dr. Achin Jain#AI #DeepLearning #MedicalImaging #SickleCellDisease #IEEE #Research #Collaboration #HealthcareInnovation

Thrilled to share that our work on Lightweight Physics Conditioned Diffusion Multi-Model for Medical Image Reconstruction has been published in Biomedical Engineering Communications (Q3). This deep learning model enhances MRI and CT image quality by integrating physics-conditioned diffusion with multi-task learning to handle noise, undersampling, and modality generalization more efficiently than traditional methods. It adapts to new tasks with minimal annotated data, making it ideal for real-world settings with limited labeled data. A huge thank you to Ganesh Kumar, Ph.D. and Shakhlokhon Kurbanova for their invaluable contributions. 🙏 Paper Link: https://lnkd.in/d23DUEEc GitHub Repository: https://lnkd.in/dS8R3brr #AIforHealthcare #MedicalImaging #DeepLearning #HealthcareInnovation #Research #MedicalTechnology

For Some Patients, the ‘Inner Voice’ May Soon Be Audible In a recent study, scientists successfully decoded not only the words people tried to say but the words they merely imagined saying. For decades, neuroengineers have dreamed of helping people who have been cut off from the world of language. A disease like amyotrophic lateral sclerosis, or A.L.S., weakens the muscles in the airway. A stroke can kill neurons that normally relay commands for speaking. Perhaps, by implanting electrodes, scientists could instead record the brain’s electric activity and translate that into spoken words. Now a team of researchers has made an important advance toward that goal. Previously they succeeded in decoding the signals produced when people tried to speak. In the new study, published on Thursday in the journal Cell, their computer often made correct guesses when the subjects simply imagined saying words. Christian Herff, a neuroscientist at Maastricht University in the Netherlands who was not involved in the research, said the result went beyond the merely technological and shed light on the mystery of language. “It’s a fantastic advance,” Dr. Herff said. The new study is the latest result in a long-running clinical trial, called BrainGate2, that has already seen some remarkable successes. One participant, Casey Harrell, now uses his brain-machine interface to hold conversations with his family and friends.   Full article with videos and pictures: https://lnkd.in/eUj8FQ_2  #als #mnd #bci #assistivetechnology #assistivetech  

𝐂𝐚𝐧 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐏𝐡𝐲𝐬𝐢𝐜𝐬 𝐇𝐞𝐥𝐩 𝐒𝐨𝐥𝐯𝐞 𝐭𝐡𝐞 𝐓𝐨𝐮𝐠𝐡𝐞𝐬𝐭 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬?🔬   Some of the most complex surgeries and diagnoses in medicine are like solving a puzzle with thousands of moving pieces — anatomy, genetics, real-time data, patient history, imaging, and uncertainty. Traditional systems struggle to connect it all fast enough. But quantum computing could change that. By using the principles of superposition and entanglement, quantum machines can process and analyze millions of variables at once — far beyond classical limits. Here’s what that means for medicine: 🧠 Better Diagnoses Quantum algorithms could analyze genomic data, MRI scans, symptoms, and risk profiles — all at the same time — to help doctors detect diseases earlier and more accurately. 🔪 Smarter Surgeries Quantum systems could simulate every possible movement in a complex surgery before a single incision is made — predicting complications, optimizing strategies, and even training surgeons with ultra-precise models. 🧬 Deeper Personalization From cancer treatments to neurological disorders, quantum computing could unlock personalized medicine by factoring in DNA, environment, and biology at scales we’ve never seen before. We’re still in the early stages, but the direction is clear: Quantum tech won’t replace doctors — it will superpower them. The future of medicine isn’t just biological. It’s quantum.🚀 #QuantumMedicine #MedicalInnovation #QuantumComputing #HealthTech #FutureOfHealthcare #PrecisionMedicine #SurgicalTech #Diagnosis #AIinHealthcare #DeepTech #EmergingTechnology #QuantumPhysics #HealthcareTransformation #GoodDoctor

🎉 Excited to share that our research paper "Efficient Machine Learning & Deep Learning Approaches for Disease Detection" has been published in IEEE Xplore following its presentation at ICCSP 2025. This work was a collaborative effort with my talented colleagues Lokesh Nuli and Sai Koushik Reddy Vatam from VIT Vellore. Special thanks to our guide Dr. Bhulakshmi Bonthu from the Department of Information Security, VIT Vellore, for her invaluable mentorship throughout the project. Our study explores the use of machine learning and deep learning techniques for early diagnosis of critical diseases like breast cancer, heart failure, pneumonia, and diabetes, achieving promising accuracy levels that can improve timely medical interventions. You can access the publication here: https://lnkd.in/gGdipygu Looking forward to continuing this journey of research and innovation! #MachineLearning #DeepLearning #HealthcareAI #Research #IEEE

🚀 Research Breakthrough Alert! 🚀 Thrilled to share that my Ph.D. scholar Mili Rosine Mathews has published her groundbreaking work in SN Computer Science (Springer Nature) 🎉 📄 Title: An Attention Driven Retinal Classification Model for the Joint Grading of Diabetic Retinopathy and Macular Edema 💡 Impact: A lightweight AI model achieving >99% accuracy in detecting & grading vision-threatening diabetic complications — paving the way for faster, cost-effective, and scalable screening in healthcare. 🔗 Read here: https://lnkd.in/gsT_augq Proud to see cutting-edge AI meeting real-world medical challenges! 👏 #ProudMentor #AIinHealthcare #DeepLearning #MedicalAI #DiabeticRetinopathy #MacularEdema #SpringerNature #ResearchInnovation

🎉 Excited to share that our paper has been published at IEEE TENSYMP held from Jul 7-9 2025 , at Christchurch , New Zealand 🎉 "HybridNet: A CNN-Transformer Based Approach for Myocardial Infarction Segmentation" Authors: Satya Karthik Achanta , Rithwik Eluri, Rama Rakshith Katta, Avasarala Nirupam Laxmi Srujan, Aneesh Narayan Bandaru, and Dr. Santosh Kumar State-of-the-Art Results: Dice Coefficient: 0.985 Weighted IoU: 0.971 F1 Score: 0.964 Classification Accuracy: 0.983 Innovation: Our HybridNet combines the power of U-Net's multi-scale feature extraction with Transformer's global attention mechanisms for accurate myocardial infarction segmentation from cardiac MRI images. Clinical Impact: 130ms inference time per image - suitable for real-time clinical deployment Addresses critical challenges: limited labeled data, complex cardiac motion, computational efficiency Outperforms traditional models (U-Net, SegNet, ResNet) and recent literature Technical Contributions: Novel hybrid CNN-Transformer architecture Advanced preprocessing pipeline with noise reduction and adaptive histogram equalization Combined Dice Loss + Cross-Entropy Loss for optimal segmentation Robust training pipeline with dynamic learning rate scheduling This work represents a significant step forward in automated cardiac diagnosis, potentially enabling faster MI detection and improved patient outcomes, especially in resource-constrained clinical settings. 📖 Read the full paper: https://lnkd.in/dKgksvBn #MachineLearning #MedicalAI #DeepLearning #CardiacImaging #ComputerVision #Healthcare #AI #Research #IEEE #Transformers #CNN #MyocardialInfarction #MedicalImageAnalysis

I am pleased to share that our research paper "Quantum-Enhanced Dual-Backbone Architecture for Accurate Gastrointestinal Disease Detection Using Endoscopic Imaging" has been accepted and published in BioMedInformatics. This work explores the integration of quantum computing with deep learning for medical image analysis, specifically focusing on gastrointestinal disease detection from endoscopic images. I would like to thank my co-authors Khidhr Halab, Professor Othmane EL MESLOUHI, Professor Zouhair Elamrani Abou Elassad, and Professor Moulay Akhloufi for their valuable contributions to this research. The full paper is available here: https://lnkd.in/e5Tgpt7s #QuantumComputing #MedicalAI #DeepLearning #Research #MedicalImaging.

We’re honored to share that #Infleqtion has been selected as a Stage 1 winner of the NIH Quantum Sensing Technology Challenge. 🔬 Our winning submission — “Rydberg atom-based THz Imaging for Disease Diagnostics and Molecular Characterization” — was the result of a powerful collaboration between Infleqtion’s quantum software and QRF teams and the University of Chicago. The project applies Infleqtion’s state-of-the-art Rydberg atom quantum sensing technology to THz medical imaging, with the goal of creating tailored machine learning models for earlier, more accurate disease screening. By harnessing the unique molecular signatures detected by these sensors, we aim to enable faster, more precise diagnosis and ultimately improve clinical outcomes. This milestone highlights the potential of quantum sensing to transform biomedical research and healthcare. 💡 #QuantumSensing #QuantumTechnology #BiomedicalResearch #HealthcareInnovation #QuantumComputing #QuantumInnovation