Today, specific CNN models assigned to specific tasks are often used. In this article, the authors have investigated three models MobileNet, EfficientNetB0 and InceptionV3. Artificial intelligence methods for deep networks will be presented but implemented in a limited computer resource. Three types of training bases were investigated, starting with a simple base verifying five colors, followed by recognition of two different orthogonal elements, and then more complex images discriminating between elements. The research aimed to demonstrate the models' capabilities based on training base parameters such as the number of images and epoch types. The architectures proposed by the authors in these cases were chosen based on simulation studies conducted on a virtual machine with limited hardware parameters. The proposals present the advantages and disadvantages of different models based on TensorFlow, Keras libraries in the Jupiter environment written based on the Python language. The authors' proposal of the selected AI model enables further work on e.g. image classification, but in a limited computer resource for industrial implementation e.g. on a PLC.

Autism Spectrum Disorder (ASD) is a neuro developmental condition that impacts social interac-tion, communication, and behavior. It is called a spectrum because its symptoms and degrees of severity can vary greatly from one person to the next. Differentiations in brain structure and function have been observed between autistic and typically developing brains. Various aspects of brain function, including information processing, sensory perception, and the capacity to interpret and respond to social cues, may be affected by these differences. The signs and symptoms of ASD are readily apparent to parents in the first few years of their child’s life. The symptoms of ASD, it is said, first appear in early childhood and continue into maturity. Brain MRI analysis utilizing deep learning methods show encouraging findings in the study of ASD. In this study, we look at how well a customized VGG16 architecture compares models that have already been trained. Inception v3, ResNet50, DenseNet121, and MobileNet are used to identify and analyze ASD problems in a brain MRI dataset from people with ASD. The proposed methods have been vali-dated on the unlabeled ABIDE 1 dataset that is freely available to the public. After the unlabeled data has been clustered, five different Deep Learning (DL) methods are used and compared. The results are compared to those of other studies that have already been conducted.

To improve the tangerine crop yield, the work of recognizing and then disposing of specific pests is becoming increasingly important. The task of recognition is based on the features extracted from the images that have been collected from websites and outdoors. Traditional recognition and deep learning methods, such as KNN (k-nearest neighbors) and AlexNet, are not preferred by knowledgeable researchers, who have proven them inaccurate. In this paper, we exploit four kinds of structures of advanced deep learning to classify 10 citrus pests. The experimental results show that Inception-ResNet-V3 obtains the minimum classification error.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset enhances the water information and reduces the data dimension. In particular, the Cloud-Free label provided in the database effectively mitigates the issue of scarcity of labeled samples Since a single form or uniform kernel size cannot handle the variety in size and shape of water bodies, we proposed the gated multi-filter inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize the attention mechanism by introducing the Gated Channel Transform (GCT) skip connection and integrating GCT into GMF- Inception to further improve model performance. Extensive experiments on three benchmarks, including WIPI, Chengdu and GF2020 datasets demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 of 0.9191, 0.7410, and 0.8421, respectively, which average F1 on three datasets is 0.0045 higher than the U-Net model and reduces 62% average GFLOPs. New WIPI dataset, code and trained models have been released on GitHub

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Abstract:The objects of fine-grained image categories(e.g., bird species) are various subclass under different categories. Because the differences between subclass are very subtle and most of them are concentrated in multiple local areas, the task of fine-grained image recognition is very challenging. At the same time, some fine-grained networks tend to focus on a certain region when judging the target category, resulting in the lack of other auxiliary regional features. To this end, Inception V3 is used as the backbone network, and an enhanced and complementary fine-grained image classification network is designed. While adopting the method of reinforcement learning to obtain more detailed fine grain image features, the complementary network can obtain the complementary discriminant area of the target through the method of attention erasure to increase the network's perception of the overall target. Finally, experiments are conducted on CUB-200-2011, FGVC Aircraft and Stanford dogs three open datasets. The experimental results show that the proposed model has better performance.

One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where, in fact, the last layers of the neural network are retrained. Therefore, the large datasets with highly unbalanced classes are not good for models’ training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2-6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes' classification also improved significantly – the TruePositive value can be increased by 0.3 and more. As result, the best approach to train considered networks on an initially unbalanced dataset was formulated.