The accurate identification of diseases on apple production is an important issue due to the worldwide importance of apple production in contemporary agriculture. Identifying diseases correctly can be challenging and affects food safety and economic loss significantly. To alleviate this, deep learning approaches, and particularly Convolutional Neural Networks (CNN), have been able to provide new and reasonable options in the agricultural field. In this study, there is a hybrid model proposed, called DenseNet-ResNet-Hybrid, which brings together architectures from DenseNet and ResNet, to provide an improvement in the extraction of features together.

This research examined the feasibility of utilizing ML algorithms to improve the initial detection and classification of anemia and other blood disorders. Multiple machine learning models (LightGBM, CatBoost, Decision Tree, Gradient Boosting, Random Forest, XGBoost) were evaluated on hematological features, with LightGBM achieving the highest accuracy.

Brain tumor detection and classification using MRI images and a convolutional neural network (CNN). The proposed model achieved 98.24% accuracy and outperformed alternative deep learning models in comparisons.

Comparative study of InceptionV4, VGG-13, and ResNet-50 for classifying lung cancer from CT images. InceptionV4 achieved 98.80% accuracy with strong precision/recall balance, suggesting suitability for clinical CAD systems.