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@article{206208,
        author = {Kale Sunil Manmath and Purushottam R Patil},
        title = {Detection of Hemorrhages of Diabetic Retinopathy Using Deep Learning and Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {649-657},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206208},
        abstract = {Globally, diabetic retinopathy (DR) is a major cause of blindness and visual impairment, especially in those with long-term diabetes. Early detection is essential for prompt intervention and efficient disease treatment since hemorrhages are a major sign of DR. A machine learning-based method for automatically identifying hemorrhages in retinal pictures is presented in this work with the goal of increasing diagnostic efficiency and accuracy. To improve retinal picture quality and retrieve pertinent characteristics, the suggested approach makes use of sophisticated image preprocessing methods. To separate hemorrhages from healthy retinal areas, a Support Vector Machine (SVM) classifier is used, and a convolutional neural network (CNN) is utilized for feature extraction. The model achieves notable accuracy, precision, and recall in identifying hemorrhages after being trained and validated using a publically accessible dataset. A comparison with conventional techniques demonstrates the suggested approach's higher performance and resilience. This automated method has the potential to help ophthalmologists diagnose patients earlier and lessen the workload associated with manual screening in environments with limited resources.},
        keywords = {Diabetic Retinopathy, Hemorrhage Detection, Machine Learning, Convolutional Neural Network (CNN), Support Vector Machine (SVM), Retinal Image Analysis, Automated Diagnosis, Medical Image Processing, Deep Learning.},
        month = {July},
        }