×
The submission system is temporarily under maintenance. Please send your manuscripts to
Go to Editorial ManagerIn recent years, there has been a considerable rise in the applications in which object or image categorization is beneficial for example, analyzing medicinal images, assisting persons to organize their collections of photos, recognizing what is around self-driving vehicles, and many more. These applications necessitate accurately labeled datasets, in their majority involve an extensive diversity in the types of images, from cats or dogs to roads, landscapes, and so forth. The fundamental aim of image categorization is to predict the category or class for the input image by specifying to which it belongs. For human beings, this is not a considerable thing, however, learning computers to perceive represents a hard issue that has become a broad area of research interest, and both computer vision techniques and deep learning algorithms have evolved. Conventional techniques utilize local descriptors for finding likeness between images, however, nowadays; progress in technology has provided the utilization of deep learning algorithms, especially the Convolutional Neural Networks (CNNs) to auto-extract representative image patterns and features for classification The fundamental aim of this paper is to inspect and explain how to utilize the algorithms and technologies of deep learning to accurately classify a dataset of images into their respective categories and keep model structure complication to a minimum. To achieve this aim, must focus precisely and accurately on categorizing the objects or images into their respective categories with excellent results. And, specify the best deep learning-based models in image processing and categorization. The developed CNN-based models have been proposed and a lot of pre-training models such as (VGG19, DenseNet201, ResNet152V2, MobileNetV2, and InceptionV3) have been presented, and all these models are trained on the Caltech-101 and Caltech-256 datasets. Extensive and comparative experiments were conducted on this dataset, and the obtained results demonstrate the effectiveness of the proposed models. The obtained results demonstrate the effectiveness of the proposed models. The accuracy for Caltech-101 and Caltech-256 datasets was (98.06% and 90%) respectively.
SARS-COV-2 (severe acute respiratory syndrome coronavirus-2) has caused widespread mortality. Infected individuals had specific radiographic visual features and fever, dry cough, lethargy, dyspnea, and other symptoms. According to the study, the chest X-ray (CXR) is one of the essential non-invasive clinical adjuncts for detecting such visual reactions associated with SARS-COV-2. Manual diagnosis is hindered by a lack of radiologists' availability to interpret CXR images and by the faint appearance of illness radiographic responses. The paper describes an automatic COVID detection based on the deep learning- based system that applied transfer learning techniques to extract features from CXR images to distinguish. The system has three main components. The first part is extracting CXR features with MobileNetV2. The second part used the extracted features and applied Dimensionality reduction using LDA. The final part is a Classifier, which employed XGBoost to classify dataset images into Normal, Pneumonia, and Covid-19. The proposed system achieved both immediate and high results with an overall accuracy of 0.96%, precision of 0.95%, recall of 0.94%, and F1 score of 0.94%.
Alzheimer’s disease (AD), the most common form of dementia, affects over 55 million people worldwide. The most form of dementia progresses into three distinct stages: mild, moderate, and very mild compared to Cognitively Normal (CN). Early detection is crucial to prevent brain damage before the late stages. Convolutional Neural Networks (CNNs), a subfield of deep learning, have recently found remarkable applications in medical image processing and computer-aided diagnosis (CAD). To this end, this paper presents a new efficient multi-classification AlzCNN-Net model to enhance the accuracy and efficacy of MRI image classification for various Alzheimer’s disease conditions. Initially, the training process involves utilizing open-source Alzheimer’s disease datasets from the Kaggle database to classify the brain MRI into its corresponding category. To verify the model’s efficacy, a comparative analysis with three pre-trained models, namely VGG16, Incep-tionV3, and MobileNetV2, has been investigated via transfer learning applied to the same dataset. As a result, the findings reveal that the AlzCNN-Net model exhibits an optimal performance, attaining the best accuracy in training with 99.67%, validation with 98.24%, and testing with 98.9% accuracy at epoch 100 with batch size 32 compared to the existing pre-trained approaches.