A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning

The classification of brain tumor is a very important section of medical diagnosis because correct and early diagnosis can greatly enhance the outcome of the patient. To aid in automated and dependable diagnostic methods, this paper examines the efficacy of already trained deep learning model to classify brain magnetic resonance imaging (MRI) scans into four categories, namely, pituitary tumor, meningioma, glioma, and no tumor. This was done using a publicly available set of 7,023 brain MRI images. DenseNet121, ResNet50, Xception and MobileNet four advanced convolutional neural network (CNN) models were also fine-tuned with transfer learning, as well as image preprocessing and data augmentation methods. Transfer learning was used to facilitate effective model adaptation with a low computation complexity and improved classification. According to the experimental findings, DenseNet121 performed better as it obtained the highest classification accuracy of 98.47% and the highest F1 score of 98.47%. The assessed models presented a high level of generalization and steady performance under a variety of evaluation criteria, which also indicates their possible application in the clinical decision support systems. These encouraging findings, however, need additional performance to improve recall on individual tumor classes, as well as the interpretability of deep-learning predictions in clinical settings. On the whole, the results highlight the great potential of the deep learning and transfer learning methods in medical image analysis, which is an opportunity to establish a more reliable, scalable and efficient solution to diagnosis. The next phase of work will involve diversifying the datasets, enhancing the model explainability, and assessing the performance of AI-oriented healthcare systems in practice to make them more widely adopted.

Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.

This study presents an improved data augmentation technique for the classification of remote sensing (RS) scenes. The method is called Quality-based Sample Selection (QSS) data augmentation technique. It is based on generating a large number of samples using geometric transformations and then selecting the best ones based on a quality criterion. Sample images are generated online, i.e. during training of the convolutional neural network (CNN) model. For each training sample image, a few images are generated randomly using a set of geometric transformations. Then, the generated images are passed to the CNN model being trained, and the predicted probabilities are used to evaluate the quality of the images. The motivation behind this research is that we aim to augment the training set with new images that will benefit the learning of the CNN model. Therefore, an objective method to evaluate the quality of the new images is the CNN model itself. The image with the best criteria score is used to augment the training set. Several quality criteria based on the prediction probabilities of the CNN model are considered including entropy, breaking-ties and our own proposed criteria called True Label Probability (TLP). QSS is tested on five common RS scene classification datasets: UCMerced, Optimal31, RSSCN7, AID and NWPU-RS45. It has outperformed all previous methods except for one case involving NWPU-RS45 dataset. For the 10%–90% train–test split, QSS achieved 94.51, 81.72, 89.89, 94.09 and 93.98, respectively, for the mentioned datasets. While for the 20%–80% train–test split, it achieved 97.09, 88.37, 94.01, 95.71 and 94.71, respectively, for the listed datasets. Thus, this work presents a novel data augmentation method that uses data with higher quality instead of randomly selected. The paper proves experimentally that the proposed method improves the classification accuracy of RS scene classification.

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN. In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning. Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94 percent, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90 percent, whereas ResNet- 50 reached 86 percent and Inception v3 obtained 64 percent accuracy. When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and reduction in losses.

ObjectiveAutomatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.MethodsThis research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.ResultsDue to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.ConclusionA deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest X-rays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

The leather industry plays a crucial role in the production of high-quality leather products to remain competitive in the market. Due to various reasons such as material handling and turning, leather defects can appear at different stages of the production process. This paper proposed deep learning techniques, specifically transfer learning, deep convolutional neural networks (D-CNN), and ensemble learning for automating leather defect detection and classification. The dataset was taken from the open source Kaggle. The dataset was preprocessed from the beginning. However, we performed Error Level Analysis (ELA) to detect possible changes or fraud in the image. We also used data augmentation techniques (resizing, rescaling, flipping, rotation, zooming, and contrasting) on the dataset to improve the model performance. This study investigated deep learning architectures for automatic leather defect detection, comparing them with seven deep convolutional neural networks (D-CNN) models and transfer learning architectures. Furthermore, we introduce and evaluate two novel ensemble models: MER built on transfer learning architecture (MobileNetV2, EfficientNetB0, and ResNet50) and AZL built on D-CNN architecture (AlexNet, ZfNet, and LeNet). A key contribution of this study is the demonstration of significant performance improvements achieved by strategically integrating squeeze and excitation and label smoothing techniques into transfer learning, D-CNN, and ensemble frameworks. Our proposed MER ensemble method achieves a state-of-the-art of 96.39% for leather defect detection. The proposed model was evaluated using performance metrics including precision, recall, F1-score, AUC, ROC, and Matthews Correlation Coefficient (MCC). Additionally, Grad-CAM was employed for explainable AI to visualize and interpret the model’s decision-making process. Comparative analysis against separate transfer learning and D-CNN models fails to establish the superior performance of our MER ensemble, which highlights the potential to significantly advance automated quality inspection in leather production. This study provides insights into evaluating the best deep learning techniques for leather defect classification, paving the way for more accurate, efficient, and reliable industrial applications. Proposes a robust deep learning-based framework for automated leather defect detection using transfer learning, deep CNNs, and two novel ensemble models (MER and AZL), enhanced with squeeze-and-excitation and label smoothing techniques. Achieves state-of-the-art performance with the proposed MER ensemble model, achieve 96.39% accuracy, and demonstrates superior results across precision, recall, F1-score, AUC–ROC, and MCC. Integrates Error Level Analysis (ELA), extensive data augmentation, and Grad-CAM–based explainable AI to ensure data reliability, improve generalization, and provide transparent visual interpretation of defect regions for industrial quality inspection.

The classification process is also used in artificial intelligence (AI), which is intelligence created by a computer, so that it can mimic actions like humans in general and can capture events that occur in the surrounding environment. Seeing the very high development of the international coffee trade, it can be concluded that if there is a type of coffee that has the best quality, it will be sought after by coffee importing countries. Batik is cloth that is painted using canting and liquid wax to form motifs or patterns that have high artistic value. Batik has a variety of unique and distinctive patterns that reflect the region where the batik motif originates. One area that has batik motifs in Indonesia is batik in the North Coastal Region of Java Island. In this area, various batiks are produced according to the characteristics of the area itself. Regarding information related to the introduction of types of batik motifs, perhaps it comes from people who have batik skills and batik craftsmen who best understand batik as a whole, while the general public does not really know about batik motifs. Because batik has different motifs and batik motifs in some areas have motifs that are almost uniform but not the same. One of the technologies that can be used is deep learning. The purpose of this study is to propose a Convolutional Neural Network (CNN) and Transfer Learning (TL) model to be implemented in an intelligent system for the process of image classification of coffee bean types. The method used in this study is the PSOCNN transfer learning model VGG16 From the results of tests carried out on 2 models, namely the CNN model, the PSOCNN transfer learning model VGG16. it was found that the highest accuracy was obtained when classifying batik motives images using the PSOCNN-transfer learning model VGG16, which was 83%. The level of accuracy that is increased when compared to the usual CNN model indicates that the use of transfer learning has a good effect on the level of accuracy obtained. Although an increase of 6% is significant, but with this increase it opens up opportunities to increase even higher by using other transfer learning models

Comparative analysis of deep convolution neural network models on small scale datasets

In the realm of computer vision, image classification is a critical issue with many applications, including multimedia content analysis, security and surveillance, and medical imaging. The accuracy of image classification algorithms has considerably increased with the development of deep learning. The discipline of image classification has dramatically benefited from the usage of pre-trained Convolutional Neural Network (CNN) models. In this study, we perform image classification on the Wang dataset composed of 1000 images separated into ten categories, using six different pre-trained CNN models. The research made use of pre-trained versions of VGG16, Densenet, Mobilenet, Inception V3, Resnet50, and Xception models. We assessed the model performances in terms of training and testing accuracy. Testing accuracies for ten unique batches of data were calculated and averaged out. Densenet outperformed state-of-the-art models like Xception by a small margin, mainly due to low data availability. The findings of this study show how pre-trained models have advanced the field of image classification and shed light on how well these models perform in diverse image classification tasks. This study can serve as a valuable reference for researchers and practitioners in the field of computer vision and deep learning and help inform their choices when selecting pre-trained models for image classification, depending on their needs, while also considering data availability and computational constraints.

Brain tumors are abnormal tissue growth characterized by uncontrolled and rapid cell proliferation. Early detection of brain tumors is critical for improving patient outcomes, and magnetic resonance imaging (MRI) has become the most widely used modality for diagnosis due to its superior image quality and non-invasive nature. Deep learning, a subset of artificial intelligence, has revolutionized automated medical image analysis by enabling highly accurate and efficient classification tasks. The objective of this study is to develop a robust and effective brain tumor detection system using MRI images through transfer learning. A diagnostic framework is constructed based on convolutional neural networks (CNN), integrating both a custom sequential CNN model and pretrained architectures, namely VGG16 and EfficientNetB4, trained on the ImageNet dataset. Prior to model training, image preprocessing techniques are applied to enhance feature extraction and overall model performance. This research addresses the common challenge of limited MRI datasets by combining EfficientNetB4 with targeted preprocessing, data augmentation, and an appropriate optimizer selection strategy. The proposed methodology significantly reduces overfitting, improves classification accuracy on small datasets, and remains computationally efficient. Unlike previous studies that focus solely on CNN or VGG16 architectures, this work systematically compares multiple transfer learning models and demonstrates the superiority of EfficientNetB4. Experimental results on the Br35H dataset show that EfficientNetB4, combined with the ADAM optimizer, achieves outstanding performance with an accuracy of 99.66%, precision of 99.68%, and an F1-score of 100%. The findings confirm that integrating EfficientNetB4 with dataset-specific preprocessing and transfer learning provides a highly accurate and cost-effective solution for brain tumor classification, facilitating rapid and reliable medical diagnosis.

Hand gestures are a form of non-verbal communication. Apart from traditional input devices, hand gestures are used for interaction with computers too. Communication via hand gestures finds many applications in the real world. Different people have hands with different shapes and orientations, which is termed as nonlinearity. The nonlinearity affects the performance of hand gesture models. A convolutional neural network (CNN) is an approach of neural networks, specifically known as deep learning. CNN is used to recognize and classify images. Sometimes, CNN could not correctly understand the hand gesture due to nonlinearity. Data augmentation helps CNN to understand the nonlinearity and complexity of images better. Data augmentation generates enormous data from lesser data, thus increasing the data adversity. Data augmentation uses various operations like zooming, rotating, shifting, shearing, and scaling to generate more data from the existing data. This article executes a CNN model using augmented data for recognition of static hand gestures. The dataset consists of 10 different hand gestures. The experimented CNN model has been trained using 10000 images and tested using 1000 images. The changes in the output of CNN with and without data augmentation have been highlighted. The CNN model employing data augmentation achieved an accuracy of 98.10%, whereas the CNN model excluding the data augmentation process attained an accuracy of 94.90% only.

As an important part of emotion research, facial expression recognition is a necessary condition for intelligent interaction between human and machine, which has an important research significance and a potential commercial value. Convolutional neural network (CNN) is an effective method to recognize facial emotions, which can perform feature extraction and classification simultaneously, and can automatically discover multiple levels of representations in data. Due to the fact that there are millions of parameters involved in training the convolutional neural network model, and there is a large demand for marked samples, transfer learning is often used to fine-tune the pre-trained model for a small target sample set. However, there are often some content differences between data sets during deep transfer learning, which will affect the recognition ability of feature extraction. In order to improve the facial expression recognition ability in transfer learning, a hybrid transfer learning model based on an improved convolution restricted boltzmann machine (CRBM) model and a CNN model is proposed in this paper. This method is fused by two learning abilities of these two models. When the pre-trained CNN model is transferred to a small target set, the CRBM is used to replace the full connection layer in the CNN model, and the CRBM layer and the sofmax layer will be retrained on the target set. The added CRBM layer can not only fully connects all feature maps, but can also learn about the unique statistical characteristics about the target set, which eliminates the influence of content differences between data sets, and extracts higher-order statistical features of facial expression images from the target set. The proposed method is evaluated based on four publicly available facial expression databases: JAFFE, FER2013, SFEW and RAF-DB. The new method can achieve better performance than most state-of-the-art methods, and it can effectively prevent the negative influence of transfer learning features between different data sets.

Rice is the staple food in Bangladesh. Every year many crops are lost because of the disease of the rice plant. Our farmers are facing a great loss every year as well as rice productivity is declining. Some diseased and healthy leaf images of rice plants have been collected. The images have been classified into four classes. The classes are Brown Spot, Hispa, Leaf Blast diseased leaves, and Healthy leaves, usually known as Oryza Saiva diseases of rice. Deep learning is very common and popular to analyze and make predictions from these kind of image data. In this research, the CNN (Convolutional Neural Network) models have been used to predict the classes. Among various CNN models, VGG16, MobileNet, ResNet50, and DenseNet121 pre-trained models have been applied. Among those pre-trained models, the highest accuracy 95.3% has been found from the VGG16 model. In addition, an Android mobile app has been developed using the highest performant VGG16 trained model. Using this mobile app, farmers will be able to upload a photo and predict the disease of that rice plant or not in a convenient way.

Deep convolutional neural networks (CNNs) enable learning trainable, highly representative and hierarchical image feature from sufficient training data which makes rapid progress in computer vision possible. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pretrained CNN features, and transfer learning , i.e., fine-tuning CNN models pretrained from natural image dataset (such as large-scale annotated natural image database: ImageNet) to medical image tasks. In this chapter, we exploit three important factors of employing deep convolutional neural networks to computer-aided detection problems. First, we exploit and evaluate several different CNN architectures including from shallower to deeper CNNs: classical CifarNet, to recent AlexNet and state-of-the-art GoogLeNet and their variants. The studied models contain five thousand to 160 million parameters and vary in the numbers of layers. Second, we explore the influence of dataset scales and spatial image context configurations on medical image classification performance. Third, when and why transfer learning from the pretrained ImageNet CNN models (via fine-tuning) can be useful for medical imaging tasks are carefully examined. We study two specific computer-aided detection (CADe) problems, namely thoracoabdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection and report the first fivefold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive quantitative evaluation, CNN model analysis, and empirical insights can be helpful to the design of high-performance CAD systems for other medical imaging tasks, without loss of generality.