en ● Artificial Intelligence مقاله اصيل پژوهشي Orginal Article <strong>Background</strong>: Diabetic retinopathy (DR) is a leading cause of blindness, and DR progression can be avoided by early dot hemorrhage (DH) detection. &nbsp;Manual diagnosis is frequently subjective and time-consuming, which emphasizes the necessity for automated methods.<br> <strong>Methods</strong>: Using machine learning (ML) and artificial intelligence (AI), this study suggests an automated DH detection system. After preprocessing retinal fundus images with contrast limited adaptive histogram equalization (CLAHE), a modified VGG-16 convolutional neural network (CNN) was used to extract features. Support vector machine (SVM), random forest (RF), and linear regression (LR) models were used to classify both local and global features.&nbsp;<br> <strong>Results</strong>: The proposed approach is highly effective in detecting and classifying DH associated with DR. When tested on the APTOS dataset, the model obtained an overall accuracy of 93.26% for DH identification and classification. This shows that the CNN is extremely effective in learning the distinguishing characteristics of hemorrhagic lesions from retinal fundus images. To confirm the suggested approach&rsquo;s efficiency, comparative research was carried out using three traditional ML algorithms: RF, SVM, and LR. Among them, the RF classifier had the greatest accuracy of 92.4%, surpassing both SVM and LR.&nbsp;<br> <strong>Conclusion</strong>: The research introduces a VGG16-based CNN that detects retinal hemorrhages in the APTOS dataset with an accuracy of 93.26%. Experimental results show that the Leaky ReLU activation function improves image classification performance, whereas the Adam and Adadelta optimizers consistently improve CNN-based learning. Finally, the research showed that deep learning-driven dynamic image analysis is a reliable and effective method for automated identification and categorization of DH, paving the way for early and accurate DR screening systems.<br> &nbsp; Dot hemorrhages (DH), Convolutional neural networks (CNN), Deep learning (DL), Machine learning (ML), Contrast limited adaptive histogram equalization (CLAHE) 723 744 http://jrh.gmu.ac.ir/browse.php?a_code=A-10-2268-3&slc_lang=en&sid=1 Aravindan Chellapandi aravindc@srmist.edu.in 100319475328460044489 100319475328460044489 No Department of Electronics and Communication Engineering, SRM Institute of Science and Technology Ramapuram Campus, Tamilnadu, India. Arthi Rengaraj arthir2@srmist.edu.in 100319475328460044490 100319475328460044490 Yes Department of Electronics and Communication Engineering, SRM Institute of Science and Technology Ramapuram Campus, Tamilnadu, India. Srisabarimani Kaliannan srisabak@srmist.edu.in 100319475328460044491 100319475328460044491 No Department of Electronics and Communication Engineering, SRM Institute of Science and Technology Ramapuram Campus, Tamilnadu, India.