Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants

In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets

Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context