Diabetic Retinopathy (DR) is an eye condition that mainly affects individuals who have diabetes and is one of the important causes of blindness in adults. As the infection progresses, it may lead to permanent loss of vision. Diagnosing diabetic retinopathy manually with the help of an ophthalmologist has been a tedious and a very laborious procedure. This paper not only focuses on diabetic retinopathy detection but also on the analysis of different DR stages, which is performed with the help of Deep Learning (DL) and transfer learning algorithms. CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet are used on a huge dataset with around 3662 train images to automatically detect which stage DR has progressed. Five DR stages, which are 0 (No DR), 1 (Mild DR), 2 (Moderate), 3 (Severe) and 4 (Proliferative DR) are processed in the proposed work. The patient’s eye images are fed as input to the model. The proposed deep learning architectures like CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet 2.1 are used to extract the features of the eye for effective classification. The models achieved an accuracy of 96.22%, 93.18% and 75.61% respectively. The paper concludes with a comparative study of the CNN, hybrid CNN with ResNet, hybrid CNN with DenseNet architectures that highlights hybrid CNN with DenseNet as the perfect deep learning classification model for automated DR detection.

Breast cancer is a deadly disease commonly affecting women. One method to avoid death from breast cancer is to obtain a diagnosis early. Breast cancer detection is a significant area that benefits from the technological advancements in artificial intelligence. A deep neural network architecture for the classification of microscopic images of breast tumor tissue acquired using excisional biopsy has been proposed. A hybrid convolutional neural network model with asymmetric convolutions and Levenberg–Marquardt optimization named as LMHistNet is used for the classification of breast cancer images into binary as well as eight subclasses. Convolution block attention module is incorporated for adaptive feature refining. Convergence time has been significantly reduced by normalizing the input features on models using batch normalization. Training is improved by reducing the internal covariance shift. Hinge loss function is used for better convergence. Magnification-dependent as well as independent binary and eight class classifications are efficiently performed by extracting diversified features from histopathological images. 7909 histopathological images of which 2480 were images of benign (normal) and the remaining images of malignant patients were used for the study. Further, performance evaluation on the dataset using LMHistNet has been analyzed based on the loss and accuracy curve. The experimental findings show that the proposed model obtained good performance over the various magnifications 40X, 100X, 200X and 400X. The accuracy, precision, recall and F1 score obtained are 88, 89, 88 and 88 respectively for multiclass classification into eight subtypes. The accuracy, precision, recall and f1 score are 99 each for binary classification of breast tumor tissues into benign and malignant classes.

This paper provides a performance comparison of different pathfinding Algorithms used in video games. The Algorithms have been classified into three categories: informed, uninformed, and metaheuristic. Both a practical and a theoretical approach have been adopted in this paper. The practical approach involved the implementation of specific Algorithms such as Dijkstra’s, A-star, Breadth First Search, and Greedy Best First. The comparison of these Algorithms is based on different criteria including execution time, total number of iterations, shortest path length, and grid size. For the theoretical approach, information was collected from various papers to compare other Algorithms with the implemented ones. The Unity game engine was used in implementing the Algorithms. The environment used was a two-dimensional grid system.