An experimental study on classification of thyroid histopathology images using transfer learning

Early and accurate identification of brain tumors is essential for determining effective treatment strategies and improving patient outcomes. Artificial intelligence (AI) and deep learning (DL) techniques have shown promise in automating diagnostic tasks based on magnetic resonance imaging (MRI). This study evaluates the performance of four pre-trained deep convolutional neural network (CNN) architectures for the automatic multi-class classification of brain tumors into four categories: Glioma, Meningioma, Pituitary, and No Tumor. The proposed approach utilizes the publicly accessible Brain Tumor MRI Msoud dataset, consisting of 7023 images, with 5712 provided for training and 1311 for testing. To assess the impact of data availability, subsets containing 25%, 50%, 75%, and 100% of the training data were used. A stratified five-fold cross-validation technique was applied. The CNN architectures evaluated include DeiT3_base_patch16_224, Xception41, Inception_v4, and Swin_Tiny_Patch4_Window7_224, all fine-tuned using transfer learning. The training pipeline incorporated advanced preprocessing and image data augmentation techniques to enhance robustness and mitigate overfitting. Among the models tested, Swin_Tiny_Patch4_Window7_224 achieved the highest classification Accuracy of 99.24% on the test set using 75% of the training data. This model demonstrated superior generalization across all tumor classes and effectively addressed class imbalance issues. Furthermore, we deployed and benchmarked the best-performing DL model on embedded AI platforms (Jetson AGX Xavier and Orin Nano), demonstrating their capability for real-time inference and highlighting their feasibility for edge-based clinical deployment. The results highlight the strong potential of pre-trained deep CNN and transformer-based architectures in medical image analysis. The proposed approach provides a scalable and energy-efficient solution for automated brain tumor diagnosis, facilitating the integration of AI into clinical workflows.

Introduction: A form of malignant tumour known as lymphoma originated in lymphoid hematopoietic organs. Because the physical characteristics of the many lymphoma classes are similar, accurately diagnosing lymphomas is one of the most difficult tasks. Hence, an efficient classification of lymphoma plays a very important role in order to provide patients with prompt care. The purpose of this work is to evaluate the performance of pre-trained Convolutional Neural Networks (CNNs) in the multiclass categorization of lymphomas. Objectives: Classification of Non-Hodgkin lymphomas by adopting pre-trained CNN architectures like ResNet50, VGG16, InceptionV3 and DenseNet201 are adopted. Utilize several pre-processing techniques for denoising, rescaling, and enriching the input images, including gaussian filter, min-max normalisation, and data augmentation. Perform a detailed performance analysis of the proposed work with existing models. Methods: This research uses the different CNN architectures such as VGG16, DenseNet201, InceptionV3 to classify the lymphoma. In pre-processing, the gaussian filter is used to denoise and smoothen the images, min-max normalization is used to rescale the images and the data augmentation is used for solving the data imbalance issue. Transfer Learning and Fine-Tuning is done which improves the overall performance of the model. Results: This study makes use of the multi cancer dataset from Kaggle. The performance of these pre-trained CNN models is evaluated using accuracy, precision, recall, and the F-measure. Based on simulation findings, DenseNet201 outperforms VGG16 and InceptionV3 with an accuracy of 99.90%. Furthermore, FFNN-ResNet50 and HPC are two current studies that are used to compare ResNet50 and DenseNet201. ResNet50-DenseNet201 has a high accuracy of 99.90% compared to FFNN-ResNet50 and HPC.   Conclusions: Several CNN architectures, including VGG16, InceptionV3 and DenseNet201 are employed in this study to categorize lymphomas. Several NHL classifications, including FL, CLL, and MCL, are classified using the pre-trained CNN architecture. The gaussian filter, which aids in smoothing the pictures, is used to eliminate noise from the histopathology images. The pixel limits are then scaled using min-max normalization to increase pixel intensity, and data augmentation is employed to prevent data imbalance problems. Improved categorization is achieved by the ResNet50 by extracting multi-scale characteristics from the images. Based on the simulation findings, it is evident that DenseNet201, which incorporates ResNet50 features, outperforms VGG16 and InceptionV3 due to the intricate interactions between data that dense connectivity enables. Furthermore, ResNet50-DenseNet201 performs better than FFNN-ResNet50 and HPC. In comparison to FFNN-ResNet50 and HPC, ResNet50-DenseNet201 has a high accuracy of 99.90%.

Purpose: The aim of the article is to develop an algorithm for classifying cracks in the analyzed images using modern methods of deep machine learning and transfer learning based on pretrained convolutional neural network - Inception-ResNet-v2. Design/Methodology/Approach: Transfer learning based on the pretrained convolutional neural network was used to categorize the images. The fully conected layer of the Inception-ResNet-v2 network has been modified. The last layer was trained using a two-class (binary) linear SVM (Support Vector Machine). In total, 20,000 training cases (images) were used to train the fully connected layer within transfer learning process. The research analyzed the possibility of using the deep neural networks for quick and fully automatic identification of cracks / defects on the surface of analyzed parts. Findings: The results indicate that pretrained convolutional neural network using SVM to train a fully connected layer is a very effective solution for visual crack / fault detection. In the analyzed model, a positive classification was obtained at the level of 99.89%. Practical Implications: The model presented in the article can be used in quality control carried out by process monitoring. An effective model for identifying defective parts can be used in both logistics and production processes. Originality/Value: A novelty is the use of a freely available, deep neural network trained to classify 1000 categories of various images for binary categorization of faults (cracks). The algorithm was adjusted by replacing the primary, 1000-output fully connected layer in the Inception-ResNet-v2 network with a binary layer (2 categories). The fully connected layer has been trained using the classification version of the popular SVM learner, but thanks to the combination of this layer with the sophisticated fearure extraction ability of the pre-trained Inception-ResNet-v2 deep network, the resulting predictive model enables the classification of defects with a very high level of accuracy.

Chapter 2 - Convolution neural network and deep learning

Recognition of human activity based on convolutional neural network has received the interest of researchers in recent years due to its significant improvement in accuracy. A large number of algorithms based on the deep learning approach have been proposed for activity recognition purpose. However, with the increasing advancements in technologies having limited computational resources, it needs to design an efficient deep learning-based approaches with improved utilization of computational resources. This paper presents a simple and efficient 2-Dimensional convolutional neural network (2-D CNN) architecture with very small size convolutional kernel for human activity recognition. The merit of the proposed CNN architecture over standard deep learning architectures is fewer trainable parameters and lesser memory requirement which enables it to train the proposed CNN architecture on low GPU memory-based devices and also works well with smaller as well as larger size datasets. The proposed approach consists of mainly four stages: namely (1) creation of dataset and data augmentation, (2) designing 2-D convolutional neural network (CNN) architecture, (3) the proposed 2-D CNN architecture trained from scratch up to optimum stage, and (4) evaluation of the trained 2-D CNN architecture. To illustrate the effectiveness of the proposed architecture several extensive experiments are conducted on three publicly available datasets, namely IXMAS, YouTube, and UCF101 dataset. The results of the proposed method and its comparison with other state-of-the-art methods [8-12,14,18-26,29-33] demonstrate the usefulness of the proposed method.

This study aimed to compare the axial cranial ultrasound images of normal and open neural tube defect (NTD) fetuses using a deep learning (DL) model and to assess its predictive accuracy in identifying open NTD.It was a prospective case-control study. Axial trans-thalamic fetal ultrasound images of participants with open fetal NTD and normal controls between 14 and 28 weeks of gestation were taken after consent. The images were divided into training, testing, and validation datasets randomly in the ratio of 70:15:15. The images were further processed and classified using DL convolutional neural network (CNN) transfer learning (TL) models. The TL models were trained for 50 epochs. The data was analyzed in terms of Cohen kappa score, accuracy score, area under receiver operating curve (AUROC) score, F1 score validity, sensitivity, and specificity of the test.A total of 59 cases and 116 controls were fully followed. Efficient net B0, Visual Geometry Group (VGG), and Inception V3 TL models were used. Both Efficient net B0 and VGG16 models gave similar high training and validation accuracy (100 and 95.83%, respectively). Using inception V3, the training and validation accuracy was 98.28 and 95.83%, respectively. The sensitivity and specificity of Efficient NetB0 was 100 and 89%, respectively, and was the best.The analysis of the changes in axial images of the fetal cranium using the DL model, Efficient Net B0 proved to be an effective model to be used in clinical application for the identification of open NTD. · Open spina bifida is often missed due to the nonrecognition of the lemon sign on ultrasound.. · Image classification using DL identified open spina bifida with excellent accuracy.. · The research is clinically relevant in low- and middle-income countries..

Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks.

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.

Background: Accurate sex estimation is crucial in forensic identification when DNA analysis is impractical or remains are fragmented. Traditional anthropometric approaches often rely on single bone measurements and yield moderate levels of accuracy. Objective: This study aimed to evaluate deep convolutional neural networks (CNNs) for automated sex estimation using entire foot radiographs, an approach rarely explored. Methods: Digital foot radiographs from 471 adults (238 men, 233 women, aged 18-65 years) without deformities or prior surgery were retrospectively collected at a single tertiary center. Six CNN architectures (AlexNet, ResNet-18, ResNet-50, ShuffleNet, GoogleNet, and InceptionV3) were trained using transfer learning (70/15/15 train-validation-test split, data augmentation). The model performance was assessed using accuracy, sensitivity, specificity, precision, and F1-score. Results: InceptionV3 achieved the highest accuracy (97.1%), surpassing previously reported methods (typically 72-89%), with balanced sensitivity (97.5%) and specificity (96.8%). ResNet-50 followed closely (95.7%), whereas simpler networks, such as AlexNet, underperformed (90%). Conclusions: Deep learning applied to whole-foot radiographs delivers state-of-the-art accuracy for sex estimation, enabling rapid, reproducible, and cost-effective forensic identification when DNA analysis is delayed or unavailable, such as in mass disasters or clinical emergency settings.

目的 基于计算机的胸腔X线影像疾病检测和分类目前存在误诊率高，准确率低的问题。本文在视觉Transformer（vision Transformer，ViT）预训练模型的基础上，通过迁移学习方法，实现胸腔X线影像辅助诊断，提高诊断准确率和效率。方法 选用带有卷积神经网络（convolutional neural network，CNN）的ViT模型，其在超大规模自然图像数据集中进行了预训练；通过微调模型结构，使用预训练的ViT模型参数初始化主干网络，并迁移至胸腔X线影像数据集中再次训练，实现疾病多标签分类。结果 在IU X-Ray数据集中对ViT迁移学习前、后模型平均AUC（area under ROC curve）得分进行对比分析实验。结果表明，预训练ViT模型平均AUC得分为0.774，与不使用迁移学习相比提升了0.208。并针对模型结构和数据预处理进行了消融实验，对ViT中的注意力机制进行可视化，进一步验证了模型有效性。最后使用Chest X-Ray14和CheXpert数据集训练微调后的ViT模型，平均AUC得分为0.839和0.806，与对比方法相比分别有0.014～0.031的提升。结论 与其他方法相比，ViT模型胸腔X线影像的多标签分类精确度更高，且迁移学习可以在降低训练成本的同时提升ViT模型的分类性能和泛化性。消融实验与模型可视化表明，包含CNN结构的ViT模型能重点关注有意义的区域，高效获取胸腔X线影像的视觉特征。;Objective The chest X-ray-relevant screening and diagnostic method is essential for radiology nowadays. Most of chest X-ray images interpretation is still restricted by clinical experience and challenged for misdiagnose and missed diagnoses. To detect and identify one or more potential diseases in images automatically，it is beneficial for improving diagnostic efficiency and accuracy using computer-based technique. Compared to natural images，multiple lesions are challenged to be detected and distinguished accurately in a single image because abnormal areas have a small proportion and complex representations in chest X-ray images. Current convolutional neural network（CNN）based deep learning models have been widely used in the context of medical imaging. The structure of the CNN convolution kernel has sensitive to local detail information，and it is possible to extract richer image features. However，the convolution kernel cannot be used to get global information，and the features-extracted are restricted of redundant information like its relevance of background， muscles，and bones. The model’s performance in multi-label classification tasks are affected to a certain extent. At present，the vision Transformer（ViT）model has achieved its priorities in computer vision-related tasks. The ViT can be used to capture information simultaneously and effectively for multiple regions of the entire image. However，it is required to use large-scale dataset training to achieve good performance. Due to some factors like patient privacy and manual annotate costs，the size of the chest X-ray image data set has been limited. To reduce the model's dependence on data scale and improve the performance of multi-label classification，we develop the CNN-based ViT pre-training model in terms of the transfer learning method for diagnosis-assisted of chest X-ray image and multi-label classification. Method The CNN-based ViT model is pre-trained on a huge scale ground truth dataset，and it is used to obtain the initial parameters of the model. The model structure is fine-tuned according to the features of chest X-ray dataset. A 1×1 convolution layer is used to convert the chest X-ray images channels between 1 to 3. The number of output nodes of the linear layer in the classifier is balanced from 1 000 to the number of chest X-ray classification labels，and the Sigmoid is used as an activation function. The parameters of the backbone network are initialized in terms of the pre-trained ViT model parameters，and it is trained in the chest X-ray dataset after that to complete multi-label classification. The experiment is configured of Python3. 7 and PyTorch1. 8 to construct the model and RTX3090 GPU for training. Stochastic gradient descent（SGD）optimizer，binary cross-entropy（BCE）loss function，an initial learning rate of 1E-3，the cosine annealing learning rate decay are used. For training，each image is scaled to a size of 512×512 pixels，and a 224×224 pixels area and it is then cropped in random as the model input，and data augmentation is performed randomly by some of the flipping，perspective transformation， shearing，translation，zooming，and changing brightness. For testing，the chest X-ray image is scaled to 256×256 pixels and center crop a 224×224 area to input the trained model. Result The experiment is performed on the IU X-Ray，which is a small-scale chest X-ray dataset. This model is evaluated in quantitative using the average of area under ROC curve （AUC）scores across all classification labels. The results show that the average AUC score of the pre-trained ViT model is 0. 774. The accuracy and training efficiency of the non-pre-trained ViT model is dropped significantly. The average AUC score is reached to 0. 566 only，which is 0. 208 lower. In addition，the attention mechanism heat map is generated based on the ViT model，which can strengthen the interpretability of the model. A series of ablation experiments are carried out for data augmentation，model structure，and batch size design. The fine-tuned ViT model is trained on the Chest-Ray14 and CheXpert dataset as well. The average AUC score is reached to 0. 839 and 0. 806，which is optimized by 0. 014 and 0. 031. Conclusion A pre-trained ViT model is used for the multi-label classification of chest X-ray images via transfer learning. The experimental results illustrate that the ViT has its stronger multi-label classification performance in chest Xray images，and its attention mechanism is beneficial for lesions precision-focused like the interior of the chest cavity and the heart. Transfer learning is potential to improve the classification performance and model generalization of the ViT in small-scale datasets，and the training cost is reduced greatly. Ablation experiments demonstrate that the incorporated model of CNN and Transformer has its priority beyond single-structure model. Data enhancement and the batch size cutting can improve the performance of the model，but smaller scale of batch is still interlinked to longer training span. To improve the model's ability，we predict that future research direction can be focused on the extraction for complex disease and highlevel semantic information，such as their small lesions，disease location，and severity.

The research on crop pest identification technology based on image analysis has important practical significance for effectively controlling the occurrence of crop diseases and insect pests (CDIP), improving crop yield and reducing the pollution of pesticides to the environment. Aiming at the problems existing in crop pest recognition technology and aiming at improving the accuracy and efficiency of crop pest recognition, this study proposes a crop pest image recognition method based on transfer learning and convolution neural network. Firstly, the crop pest image is geometrically operated to expand the crop pest image data set, and then the expanded data set is divided into training set and test set. Then, Alexnet, VGG-16, and ResNet-50 models are pretrained on ImageNet large image data set. Based on the theory of transfer learning, the learned model parameters are transferred to the small sample image data set of crop diseases and insect pests in this paper, so as to train the crop diseases and insect pests recognition model. This method is tested on Image Database for Agricultural Diseases and Pests (IDADP). The experimental results show that the convolution neural network plant leaf image recognition method based on transfer learning has better effect. It can quickly and accurately diagnose crop diseases, reduce the use of pesticides and fertilizers, and improve the yield and quality of crops.

Pneumonia as a disease worsens due to environmental pollution and impacts especially children, elderly, and weak immune system patients. The proper and timely diagnosis of pneumonia is very important as it reduces the risk factors that are likely to occur due to pneumonia This paper seeks to eradicate these issues using CNN architectures. pneumonia can be classified into three different types, namely, Bacterial pneumonia, Viral pneumonia, and Fungal pneumonia; the task of this thesis is to automatically detect these types of pneumonia from images of Chest X-ray. CNN (Convolutional Neural Networks) using system-aided technology of deep learning has brought significant changes in the classification of pneumonia images from x-ray of chest over the last decade. The model which was trained on a dataset of 20 thousands images having a size of (224 x 224) pixels and tested a group size of 32 provided an astounding accuracy of 95%. Two main limitations of existing models are high computational costs and logistical. Incorporating advanced algorithms such as transfer learning, pre-trained models and deep convolutional neural networks overcomes these limitations. Such AI-supporting medical systems are very effective not only to improve the early and accurate diagnosis but also to improve the availability of health care systems in developing countries

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.

Brain tumor classification using medical images is crucial for identification and therapy. However, brain tumors are complex and vary, making grouping them difficult. This work demonstrates a novel transfer learning method for brain tumor classification. We employ trained Convolutional Neural Networks (CNNs) models and data enrichment approaches to extract meaningful information from medical images. We want to fine-tune the models built on our dataset to uncover hierarchical patterns that distinguish tumor types. Through data enrichment, the training sample becomes more diverse and richer, making the model more generic and robust. Our team's extensive testing and research have shown that the suggested procedure can identify brain tumors. Our machine-learning approach performs better than others in terms of accuracy, sensitivity, specificity, and precision. Our technique improves brain tumor categorization and assures accurate clinical diagnosis. Automated testing systems are one way for physicians to assist patients in selecting the best course of treatment. Researchers may improve classification performance by incorporating modern imaging technology or topic-specific data. The Internet of Things, or IoT, is helping to drive the development of complex real-time data collection, processing, and sharing systems. These technological advancements have transformed medical imaging. This graphic depicts a cutting-edge transfer learning system that may be able to identify brain cancer from medical photos. This technology has the potential to enhance data collection and processing via the Internet of Things. Data augmentation and pre-trained convolutional neural networks may help to extract interpretable medical images. The Internet of Things improved the model's flexibility, resilience, and utility. We achieved this by expanding the training data set. Rapid categorization advancements have made clinical diagnosis more efficient. Classification, deep learning, medical imaging, machine learning, transfer learning, tumor detection, and image analysis all relate to this topic.

Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading