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Machine vision-based inspection technologies are gaining considerable importance for automated monitoring and quality control of manufactured products in recent years due to the advent of Industry 4.0. The involvement of advanced deep learning methods is a significant factor contributing to the advent of robust vision-based solutions for improving inspection accuracy at a significantly lower cost in manufacturing industries. The requirement of computational resources and large training datasets hinders the deployment of these solutions to manufacturing shop floors. The present research work develops an image-based framework considering pre-trained Convolutional Neural Network (CNN), ResNet-101 to detect surface defects with the minimum training datasets and computational requirements. The outcomes of the proposed framework are substantiated through a case study of detecting commonly observed surface defects during the centerless grinding of tapered rollers. The image datasets consisting of standard tapered rollers and three common defect classes are captured and enriched further with the help of the data augmentation technique. The present work employs ResNet-101 for feature extraction combined with and multi-class Support Vector Machine (SVM) as a classifier to detect defective images. The effects of the feature extraction layer (fc1000) and pooling layer (pool5) activation are explored to achieve the desired prediction abilities. The testing trials demonstrate that the proposed framework effectively performs image classification, achieving 100% precision for the ‘Good’ class components. The study showed that the proposed approach could overcome the requirements of large training datasets and higher computational power for deep learning models. The proposed system can be of significant importance for Micro, Small, and Medium Enterprises (MSMEs) and Small and Medium-sized Enterprises (SMEs) as an alternative to conventional labor-intensive manual inspection techniques.

Bangladesh and India are significant paddy-cultivation countries in the globe. Paddy is the key producing crop in Bangladesh. In the last 11 years, the part of agriculture in Bangladesh's Gross Domestic Product (GDP) was contributing about 15.08 percent. But unfortunately, the farmers who are working so hard to grow this crop, have to face huge losses because of crop damages caused by various diseases of paddy. There are approximately more than 30 diseases of paddy leaf and among them, about 7-8 diseases are quite common in Bangladesh. Paddy leaf diseases like Brown Spot Disease, Blast Disease, Bacterial Leaf Blight, etc. are very well known and most affecting one among different paddy leaf diseases. These diseases are hampering the growth and productivity of paddy plants which can lead to great ecological and economical losses. If these diseases can be detected at an early stage with great accuracy and in a short time, then the damages to the crops can be greatly reduced and the losses of the farmers can be prevented. This paper has worked on 4 types of diseases and one healthy leaf class of the paddy. The main goal of this paper is to provide the best results for paddy leaf disease detection through an automated detection approach with the deep learning CNN models that can achieve the highest accuracy instead of the traditional lengthy manual disease detection process where the accuracy is also greatly questionable. It has analyzed four models such as VGG-19, Inception-Resnet-V2, ResNet-101, Xception, and achieved better accuracy from Inception-ResNet-V2 is 92.68%.

Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.

Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

In this paper, we use Caffe framework to implement the improved Faster R-CNN recognition technique for building images in civil engineering under Linux system and add feature pyramid network and regional feature aggregation into the ResNet-50 network and ResNet-101 network, respectively, to strengthen the training effect, and establish ResNet-101+FPN+ROI Align image recognition technique. Simulated crack experiments and concrete surface quality defect detection experiments confirm that the ResNet-101 FPN ROI Align method is accurate and detects defects at a high rate. The method established in this paper has a minimum error of only 0.4% in the simulated crack experiment, and the detection rate is much higher than that of other detection methods when detecting quality defects on the concrete surface, and the accuracy can reach up to 94% at the same time. In civil engineering, the image recognition technology established in this paper has practical significance and high application value, as demonstrated by the experiment.

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher ( P  < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher ( P  < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.

The lungs are two of the most crucial organs in the human body because they are connected to the respiratory and circulatory systems. Lung cancer, COVID-19, pneumonia, and other severe diseases are just a few of the many threats. The patient is subjected to an X-ray examination to evaluate the health of their lungs. A radiologist must interpret the X-ray results. The rapid advancement of technology today can help people in many different ways. One use of deep learning in the health industry is in the detection of diseases, which can decrease the amount of money, time, and energy needed while increasing effectiveness and efficiency. There are other methods that can be used, but in this research, the convolutional neural network (CNN) method is only used with three architectures, namely ResNet-50, ResNet-101, and ResNet-152, to aid radiologists in identifying lung diseases in patients. The 21,885 images that make up the dataset for this study are split into four groups: COVID-19, pneumonia, lung opacity, and normal. The three algorithms have fairly high evaluation scores per the experiment results. F1 scores of 91%, 93%, and 94% are assigned to the ResNet-50, ResNet-101, and ResNet-152 architectures, respectively. Therefore, it is advised to use the ResNet-152 architecture, which has better performance values than the other two designs in this study, to categorize lung diseases experienced by patients.

Polymicrogyria (PMG) is a multifaceted neurological disorder caused by abnormal cortical folding, mostly in children. It commonly results in developmental delays, seizures, and motor weakness. The mild features of PMG in neuroimaging often make its identification difficult, even for experts. In this paper, we assess the efficacy of various advanced image preprocessing strategies on the overall performance of Convolutional Neural Network (CNN) applied for PMG diagnosis in MRI brain scans. We employ a pre-processing sequence that includes Min–Max normalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Bilateral filtering, and Canny edge detection aimed at improving the recognition of subtle features without losing essential details. The techniques can enhance the visualization of delicate structural deformities in the brain MRI images and assist in the diagnosis of neurological disorders by clinicians. Experimental results suggest that performance enhancement was achieved with all of the tested CNN architectures. ResNet-101 has exhibited the most remarkable accuracy enhancement by 10.3%. ResNet and VGG architectures delivered much greater performance improvement as compared to MobileNetV2 and DenseNet-201 models. GradCAM++ is adopted to infer the decision-making mechanism of the considered deep learning architectures. The methodology finds applications in neurological imaging and may be used to assist healthcare providers in the diagnosis of polymicrogyria. Our findings emphasize the crucial role of image pre-processing techniques in increasing the capabilities of deep learning frameworks to assist with complex tasks in medical image analysis.

Sarcasm is a sophisticated method to convey ideas. Usually, the literal meaning of a sarcasm message is the opposite of its true intent. The development of social platforms has enriched the way users express their thoughts. User-posted information now incorporates not only text but also images. Traditional Sarcasm detection methods rely solely on textual data, failing to leverage the valuable information provided by images. This limitation leads to incomplete information for sarcasm detection, thereby compromising the accuracy of detection results. To address this, the paper proposes a new image-text fusion Transformer network (ITFT-Net) for sarcasm detection. This model uses the Bidirectional Encoder Representations from Transformers (BERT) model to extract text features. Additionally, it introduces the ResNet-101 model with a Transformer Encoder block to extract image features. Due to the lack of adaptive correlation in multimodal feature fusion, a multimodal fusion Transformer Encoder (MFTE) module is designed to enhance the fusion of the image and text features. Finally, the fusion features, processed by the Transformer Encoder module, is utilized for prediction. Experimental results on public datasets have demonstrated that the proposed model outperforms the baseline model in terms of accuracy and F1 value by 0.75% and 0.69% respectively.