Computer-aided Diagnosis Method Research Articles

Exploring the Efficacy of Various CNN Architectures in Diagnosing Oral Cancer from Squamous Cell Carcinoma

Oral cancer can result from mutations in cells located in the lips or mouth. Diagnosing oral cavity squamous cell carcinoma (OCSCC) is particularly challenging, often occurring at advanced stages. To address this, computer-aided diagnosis methods are increasingly being used. In this work, a deep learning-based approach utilizing models such as VGG16, ResNet50, LeNet-5, MobileNetV2, and Inception V3 is presented. NEOR and OCSCC datasets were used for feature extraction, with virtual slide images divided into tiles and classified as normal or squamous cell cancer. Performance metrics like accuracy, F1-score, AUC, precision, and recall were analyzed to determine the prerequisites for optimal CNN performance. The proposed CNN approaches were effective for classifying OCSCC and oral dysplasia, with the highest accuracy of 95.41% achieved using MobileNetV2. Key findingsDeep learning models, particularly MobileNetV2, achieved high classification accuracy (95.41%) for OCSCC.CNN-based methods show promise for early-stage OCSCC and oral dysplasia diagnosis. Performance parameters like precision, recall, and F1-score help optimize CNN model selection for this task.

Multi-label semantic segmentation of magnetic resonance images of the prostate gland

Prostate segmentation is a substantial factor in the diagnostic pathway of suspicious prostate lesions. Medical doctors are assisted by computer-aided detection and diagnosis systems systems and methods derived from artificial intelligence deep learning-based systems have to be trained on existing data. Especially freely available labeled prostate magnetic resonance imaging data is rare. With regard to this problem, we show a method to combine two existing small datasets to form a bigger one. We present a data processing pipeline consisting of a cascaded network architecture that is able to perform multi-label semantic segmentation, hence, capable of classifying each pixel in a T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{T}_{2}$$\\end{document}-weighted image not only to one class but to a subset of the prostate zones and classes of interest. This delivers richer information such as overlapping zones that are key to medical radiological examination. Additionally, we describe how to integrate expert knowledge in our deep learning system. To increase data variety for training and evaluation we use image augmentation on our two datasets—a freely available dataset and our new open-source dataset. To combine the datasets we denoise the contourings in our dataset by using an effective yet simple algorithm based on standard computer vision methods only. The performance of the presented methodology is compared and evaluated using the dice score metric and 5-fold cross-validation on all datasets. Although we trained on tiny datasets our method achieves excellent segmentation quality and is even able to detect prostate cancer. Our method to combine the two datasets reduces segmentation errors and increases data variety. The proposed architecture significantly improves performance by including expert knowledge via feature-map concatenation. On the initiative for collaborative computer vision benchmarking dataset we achieve on average dice scores of approximately 91% for the whole prostate gland, 67% for the peripheral zone and 75% for the prostate central gland. We find that image augmentation except contrast limited adaptive histogram equalisation did not have much influence on the segmentation quality. Derived and enhanced from existing methods we present an approach that is able to deliver multi-label semantic segmentation results for prostate magnetic resonance imaging. This approach is simple and could be applied to other applications of deep learning as well. It improves the segmentation results by a large margin. Once tweaked to the data, our denoising and combination algorithm delivers robust and accurate results even on data with segmentation errors.

Efficient clinical decision-making process via AI-based multimodal data fusion: A COVID-19 case study

COVID-19 is an infectious disease that caused a global pandemic in 2020. In the critical moments of this healthcare emergencies, the medical staff needs to take important decisions in a context of limited resources that must be carefully managed. To this end, the computer-aided diagnosis methods are extremely powerful and help them to better recognize the evidences of high-risk patients. This can be done with the support of relevant information extracted from electronic health records, lab tests and imaging studies. In this work, we present a novel fully-automatic efficient method to help the clinical decision-making process in the context of COVID-19 risk estimation, using multimodal data fusion of clinical features and deep features extracted from chest X-ray images. The risk estimation is studied in two of the most relevant and critical encountered scenarios: the risk of hospitalization and mortality. This study shows which are the most important features for each scenario, the ratio of clinical and imaging features present in the top ranking and the performance of the used machine learning models. The results demonstrate a great performance by the classifiers, estimating the risk of hospitalization with an AUC-ROC of 0.8452 ± 0.0133 and the risk of death with an AUC-ROC of 0.8285 ± 0.0210, only using a subset of the original features, and highlight the significant contribution of imaging features to hospitalization risk assessment, while clinical features become more crucial for mortality risk evaluation. Furthermore, multimodal data fusion can outperform the approaches that use one data source. Despite the model's complexity, it requires fewer features, an advantage in scenarios with limited computational resources. This streamlined, fully-automated method shows promising potential to improve the clinical decision-making process and better manage medical resources, not only in the context of COVID-19, but also in other clinical scenarios.

Computer-aided diagnosis of pituitary microadenoma on dynamic contrast-enhanced MRI based on spatio-temporal features

Computer-aided diagnosis (CAD) of pituitary microadenoma (PM) can assist doctors in decision-making, leading to improved lesion detection rates and diagnostic accuracy. However, the performance of existing CAD methods for PM detection has been hindered by the difficulty in obtaining high-quality segmentation results. This is primarily due to the small size of PM lesions and the relatively low resolution of Magnetic Resonance Imaging (MRI) images. To address these challenges, this paper proposes a new medical image detection and segmentation model based on spatio-temporal information. The proposed model aims to addresses the disease classification of PM by designing a network module based on multi-scale feature fusion. This module ensures comprehensive extraction of target semantic information while retaining clear spatial information, achieving classification from dynamic contrast-enhanced MRI(DCE-MRI) to identify positive PM samples. For the lesion segmentation of PM, after ROI Align alignment, the model further adds a semantic segmentation module named Dual-path Semantic Segmentation Module (DSSM) behind the mask head and classification head. This module captures more precise spatio-temporal semantic information, reducing accuracy loss and achieving pituitary segmentation. Finally, leveraging the results of pituitary detection, a feature pyramid network (FPN) layer is redesigned named Reuse Underlying Information Module (RUIM) to reuse low-level information, enhancing the detection capability for PM and thus achieving precise object detection and segmentation. The proposed model achieves an accuracy of 97.10% for PM, mAP of 50.24%, which is superior to multiple representative deep models for medical data. The code is available at https://github.com/BUCT-IUSRC/Research__PM-CAD.

Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

STU3Net: An Improved U‐Net With Swin Transformer Fusion for Thyroid Nodule Segmentation

ABSTRACTThyroid nodules are a common endocrine system disorder for which accurate ultrasound image segmentation is important for evaluation and diagnosis, as well as a critical step in computer‐aided diagnostic systems. However, the accuracy and consistency of segmentation remains a challenging task due to the presence of scattering noise, low contrast and resolution in ultrasound images. Therefore, we propose a deep learning‐based CAD (computer‐aided diagnosis) method, STU3Net in this paper, aiming at automatic segmentation of thyroid nodules. The method employs a modified Swin Transformer combined with a CNN encoder, which is capable of extracting morphological features and edge details of thyroid nodules in ultrasound images. In decoding through the features for image reconstruction, we introduce a modified three‐layer U‐Net network with cross‐layer connectivity to further enhance image reduction. This cross‐layer connectivity enhances the network's capture and representation of the contained image feature information by creating skip connections between different layers and merging the detailed information of the shallow network with the abstract information of the deeper network. Through comparison experiments with current mainstream deep learning methods on the TN3K and BUSI datasets, we validate the superiority of the STU3Net method in thyroid nodule segmentation performance. The experimental results show that STU3Net outperforms most of the mainstream models on the TN3K dataset, with Dice and IoU reaching 0.8368 and 0.7416, respectively, which are significantly better than other methods. The method demonstrates excellent performance on these datasets and provides radiologists with an effective auxiliary tool to accurately detect thyroid nodules in ultrasound images.

Comparative Analysis of Pneumonia Detection from Chest X-Ray Images Using CNN And Transfer Learning

A widespread bacterial or viral infection of the respiratory tract, pneumonia affects many people. particularly in developing and impoverished countries where pollution, unsanitary living conditions, and overcrowding are all too common, as well as a lack of medical infrastructure. Pneumonia produces pleural effusion, which is a condition in which fluids fill the lungsand create breathing problems. Early detection of pneumonia is critical for ensuring a cure and improving survival rates. The most common method for detecting pneumonia is chest X-ray imaging. As opposed to that, examining chest X-rays can be challenging and vulnerable to subjective fluctuation. A computer-aided diagnosis method for automatic pneumonia detection utilizing This research includes the creation of chest Images from X-rays. To evaluate which model is superior, an experiment was conducted utilizing a publicly accessible database on all three models. A Convolutional Neural Network (CNN) model was developed to address the lack of readily available data. together using transfer learning strategies like Mobile Net and VCG. On a dataset of accessible pneumonia X-rays, the method was tested. This research shows which neural network algorithm is optimal for detecting pneumonia, and how medical practitioners might use it in the actual world. Keywords: Pneumonia, Chest X-ray, Deep Learning, Convolutional Neural Network (CNN), Mobile Net, VCG, ReLU, Max pooling.

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named “Skin-CAD” which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly.

SKIN CANCER DETECTION USING CNN

The skin is the main component of a human body and serves to cover all of the body's muscles, bones, and other structures. Many people these days are afflicted with skin cancer. Skin cancer that is most deadly is called malignant melanoma. Melanoma is the most dangerous kind of cancer. It is a massive malignant tumour on the skin, and its spread is growing daily. If melanoma is discovered early on, treatment is straightforward. Melanoma diagnosis involves both automated and clinical procedures. Early detection of malignant melanoma has significant potential with image-based computer aided diagnosis methods. Automatically identifying the type of skin cancer from the pictures can help with a speedier diagnosis and improved accuracy.

U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.

U-Net Segmentation for CNN Classification to Improve the Accuracy of Automatic Prediction of Brain Tumors Using MRI Images

Among various cancers, a brain tumor is a serious one that affects the patient's brain and causes sudden death. Several clinical diagnoses of brain tumors help medical experts provide the proper treatment at the right time; earlier identification and detection of brain tumors are much less. With the increase of cancer patients, managing and diagnosing efficiently with massive data collected is complex. Diagnosing patients with a brain tumor in the earlier stages is also tricky. Medical image processing methods and computer-aided diagnosis methods proposed in earlier works have not provided a high level of accuracy in classification, but the segmentation accuracy was good. Some recent research works have focused on implementing machine learning algorithms for predicting brain tumors for a massive amount of data, where the accuracy still needs to be improved. Machine learning and deep learning algorithms are used to predict brain tumors and their types earlier. With a large amount of data available, the ML and DL models are trained efficiently to make accurate prediction models. The DL models provide better accuracy in processing and predicting medical images than ML models. Some pre-trained deep learning algorithms like VGG16, VGG19, InceptionV3, and ImageNet provide improved accuracy and less computation power to achieve the required results. This paper proposes a CNN algorithm with a VGG16 pre-trained model for detecting brain tumors with MRI images. The input images are segmented using a U-Net model, improving the prediction process's accuracy, and are classified using a CNN model with VGG16, which helps in the efficient and faster prediction of brain tumors from the MRI images. The results show that the proposed model provides better accuracy than the existing ones.

Residual cosine similar attention and bidirectional convolution in dual-branch network for skin lesion image classification

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper, we propose a dual-branch neural network model Conformer with Residual Cosine Similarity Attention and Bidirectional Convolutional fusion strategy, named RCSABC-Conformer. The core of this network structure comprises three parts: a Convolutional Neural Network (CNN) branch with Residual Cosine Similarity Attention (RCSA), a Transformer branch, and a Feature Couple Unit with Bidirectional Convolutional strategy (BC-FCU). The RCSA module calculates the cosine similarity value between the feature map generated by the convolutional operation and the feature map of the residual edge to assess whether their semantic information is similar. The semantic information of similar parts is weighted by exponential normalization to enhance the network's memory of similar features of the same type of skin lesion. The BC-FCU module interactively fuses local features and global representations of skin lesion images with different resolutions in the two branches. Specifically, when the global representations is integrated into local features, we introduce a new bidirectional convolution strategy to extract the feature map from both forward and backward directions, and then select the element with the smaller feature value from the two directions to fuse into local features. In this way, we can minimize the interference of the artifact features extracted by the Transformer branch on the CNN branch. In addition, taking advantage of the Transformer branch's capacity to construct global representations, our model can learn contextual semantic information of normal skin and lesion areas to enhance model robustness. We conducted experiments on three datasets, consisting of clinical and dermoscopic skin lesion images, as well as a hybrid of both. The experimental results show that RCSABC-Conformer outperforms both advanced and classical classification methods in terms of classification accuracy across all three datasets, without requiring an increase in the number of parameters and computational complexity. Compared with the baseline model, the classification accuracy of our proposed method improves by 2.40%, 5.39%, and 4.44% on the three datasets, respectively. To the best of our knowledge, this is the first study to apply an interactive fusion dual-branch network for multi-disease classification on different modalities of skin lesion databases. Code will be available at https://github.com/AlenLi817/RCSABC-Conformer.

An Overview of Current Research on Computer-Aided Liver Diagnosis Based on Computed Tomography Employing Deep Learning Methods

Abstract: Compared to traditional X-rays, computed tomography (CT) scanning is a non-invasive diagnostic imaging method that yields more precise information about the liver. In contrast to ultrasonography (US) exams, the quality of a CT scan is not heavily operator dependent. Many studies have been conducted with improved outcomes utilizing traditional machine learning methods for computer-aided diagnosis (CAD) of the liver. Recent developments, particularly in the field of deep learning technology, have made it possible to identify, categorize, and segment patterns in medical images. These developments have also been used to other fields outside of medicine. The ability of deep learning to automatically learn feature representations from data, as opposed to feeding in manually created features based on application, is one of its fundamental capabilities. The fundamentals of deep learning are presented in this paper, along with their achievements in liver segmentation and lesion detection, classification utilizing CT imaging modalities, and discussion of their various network topologies. Another fascinating deep learning strategy that is covered is transfer learning. Thus, deep learning and the CAD system have had a significant influence and improved the performance of the healthcare sector.

Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images

Respiratory diseases have a significant global impact, and assessing these conditions is crucial for improving patient outcomes. Chest X-ray is widely used for diagnosis, but expert evaluation can be challenging. Automatic computer-aided diagnosis methods can provide support for clinicians in these tasks. Deep learning has emerged as a set of algorithms with exceptional potential in such tasks. However, these algorithms require a vast amount of data, often scarce in medical imaging domains. In this work, a new data augmentation methodology based on adapted generative latent diffusion models is proposed to improve the performance of an automatic pathological screening in two high-impact scenarios: tuberculosis and lung nodules. The methodology is evaluated using three publicly available datasets, representative of real-world settings. An ablation study obtained the highest-performing image generation model configuration regarding the number of training steps. The results demonstrate that the novel set of generated images can improve the performance of the screening of these two highly relevant pathologies, obtaining an accuracy of 97.09%, 92.14% in each dataset of tuberculosis screening, respectively, and 82.19% in lung nodules. The proposal notably improves on previous image generation methods for data augmentation, highlighting the importance of the contribution in these critical public health challenges.Graphical abstract

MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.

Lung cancer is one of the leading causes of cancer-related deaths worldwide. To reduce the mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities of medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called the Mitscherlich function-based Ensemble Network (MENet), which combines the prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, and MobileNetV2, to improve the accuracy of a lung cancer prediction model. The ensemble approach is based on the Mitscherlich function, which produces a fuzzy rank to combine the outputs of the said base classifiers. The proposed method is trained and tested on the two publicly available lung cancer datasets, namely Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) and LIDC-IDRI, both of these are computed tomography (CT) scan datasets. The obtained results in terms of some standard metrics show that the proposed method performs better than state-of-the-art methods. The codes for the proposed work are available at https://github.com/SuryaMajumder/MENet.

Ensemble Learning-Based Solutions: An Approach for Evaluating Multiple Features in the Context of H&E Histological Images

In this paper, we propose an approach based on ensemble learning to classify histology tissues stained with hematoxylin and eosin. The proposal was applied to representative images of colorectal cancer, oral epithelial dysplasia, non-Hodgkin’s lymphoma, and liver tissues (the classification of gender and age from liver tissue samples). The ensemble learning considered multiple combinations of techniques that are commonly used to develop computer-aided diagnosis methods in medical imaging. The feature extraction was defined with different descriptors, exploring the deep learning and handcrafted methods. The deep-learned features were obtained using five different convolutional neural network architectures. The handcrafted features were representatives of fractal techniques (multidimensional and multiscale approaches), Haralick descriptors, and local binary patterns. A two-stage feature selection process (ranking with metaheuristics) was defined to obtain the main combinations of descriptors and, consequently, techniques. Each combination was tested through a rigorous ensemble process, exploring heterogeneous classifiers, such as Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. The ensemble learning presented here provided accuracy rates from 90.72% to 100.00% and offered relevant information about the combinations of techniques in multiple histological images and the main features present in the top-performing solutions, using smaller sets of descriptors (limited to a maximum of 53), which involved each ensemble process and solutions that have not yet been explored. The developed methodology, i.e., making the knowledge of each ensemble learning comprehensible to specialists, complements the main contributions of this study to supporting the development of computer-aided diagnosis systems for histological images.

Deep learning-based immunohistochemical estimation of breast cancer via ultrasound image applications.

Breast cancer is the key global menace to women's health, which ranks first by mortality rate. The rate reduction and early diagnostics of breast cancer are the mainstream of medical research. Immunohistochemical examination is the most important link in the process of breast cancer treatment, and its results directly affect physicians' decision-making on follow-up medical treatment. This study aims to develop a computer-aided diagnosis (CAD) method based on deep learning to classify breast ultrasound (BUS) images according to immunohistochemical results. A new depth learning framework guided by BUS image data analysis was proposed for the classification of breast cancer nodes in BUS images. The proposed CAD classification network mainly comprised three innovation points. First, a multilevel feature distillation network (MFD-Net) based on CNN, which could extract feature layers of different scales, was designed. Then, the image features extracted at different depths were fused to achieve multilevel feature distillation using depth separable convolution and reverse depth separable convolution to increase convolution depths. Finally, a new attention module containing two independent submodules, the channel attention module (CAM) and the spatial attention module (SAM), was introduced to improve the model classification ability in channel and space. A total of 500 axial BUS images were retrieved from 294 patients who underwent BUS examination, and these images were detected and cropped, resulting in breast cancer node BUS image datasets, which were classified according to immunohistochemical findings, and the datasets were randomly subdivided into a training set (70%) and a test set (30%) in the classification process, with the results of the four immune indices output simultaneously from training and testing, in the model comparison experiment. Taking ER immune indicators as an example, the proposed model achieved a precision of 0.8933, a recall of 0.7563, an F1 score of 0.8191, and an accuracy of 0.8386, significantly outperforming the other models. The results of the designed ablation experiment also showed that the proposed multistage characteristic distillation structure and attention module were key in improving the accuracy rate. The extensive experiments verify the high efficiency of the proposed method. It is considered the first classification of breast cancer by immunohistochemical results in breast cancer image processing, and it provides an effective aid for postoperative breast cancer treatment, greatly reduces the difficulty of diagnosis for doctors, and improves work efficiency.

RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

Co-Mask R-CNN: collaborative learning-based method for tooth instance segmentation.

Traditional tooth image analysis methods primarily focus on feature extraction from individual images, often overlooking critical tooth shape and position information. This paper presents a novel computer-aided diagnosis method, Collaborative learning with Mask Region-based Convolutional Neural Network (Co-Mask R-CNN), designed to enhance tooth image analysis by leveraging the integration of complementary information. First, image enhancement is employed to generate an edge-enhanced tooth edge image. Then, a collaborative learning strategy combined with Mask R-CNN is introduced, where the original and edge images are input simultaneously, and a two-stream encoder extracts feature maps from complementary images. By utilizing an attention mechanism, the output features from the two branches are dynamically fused, quantifying the relative importance of the two complementary images at different spatial positions. Finally, the fused feature map is utilized for tooth instance segmentation. Extensive experiments are conducted using a proprietary dataset to evaluate the effectiveness of Co-Mask R-CNN, and the results are compared against those of an alternative segmentation network. The results demonstrate that Co-Mask R-CNN outperforms the other networks in terms of both segmentation accuracy and robustness. Consequently, this method holds considerable promise for providing medical professionals with precise tooth segmentation results, establishing a reliable foundation for subsequent tooth disease diagnosis and treatment.

Recent Advances in PCG Signal Analysis using AI: A Review

Abstract The paper reviews the milestones and various modern-day approaches in developing phonocardiogram (PCG) signal analysis. It also explains the different phases and methods of the Heart Sound signal analysis. Many physicians depend heavily on ECG experts, inviting healthcare costs and ignorance of stethoscope skills. Hence, auscultation is not a simple solution for the detection of valvular heart disease; therefore, doctors prefer clinical evaluation using Doppler Echo-cardiogram and another pathological test. However, the benefits of auscultation and other clinical evaluation can be associated with computer-aided diagnosis methods that can help considerably in measuring and analyzing various Heart Sounds. This review covers the most recent research for segmenting valvular Heart Sound during preprocessing stages, like adaptive fuzzy system, Shannon energy, time-frequency representation, and discrete wavelet distribution for analyzing and diagnosing various heart-related diseases. Different Convolutional Neural Network (CNN) based deep-learning models are discussed for valvular Heart Sound analysis, like LeNet-5, AlexNet, VGG16, VGG19, DenseNet121, Inception Net, Residual Net, Google Net, Mobile Net, Squeeze Net, and Xception Net. Among all deep-learning methods, the Xception Net claimed the highest accuracy of 99.43 + 0.03% and sensitivity of 98.58 + 0.06%. The review also provides the recent advances in the feature extraction and classification techniques of Cardiac Sound, which helps researchers and readers to a great extent.