Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

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Object detection from images and videos is the main point in the applications of artificial intelligence and computer vision like self-driving cars, robotics etc. In this paper, we have proposed a way to detect the objects in images and videos by a new pre-training strategy through convolutional neural network with deep learning. We are using the reLU, pooling and fully connected layer methods to increase the accuracy in detecting the objects and the number of detecting objects has increased. We have used coco database in which it has different types of object names with its threshold which are highly used for detecting the objects. We have used 3 different ways of input for detecting the objects which are images, videos and live camera. The algorithm used is regression. We have used YOLO v3 which uses the single neural network and divides the image into regions and predicts the objects.