Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN.

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification tasks. However, deep learning has the following problems in medical image classification. First, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weights of deep learning models are not well optimized. Therefore, this paper starts from the perspective of network optimization and improves the nonlinear modeling ability of the network through optimization methods. A new network weight initialization method is proposed, which alleviates the problem that existing deep learning model initialization is limited by the type of the nonlinear unit adopted and increases the potential of the neural network to handle different visual tasks. Moreover, through an in‐depth study of the multicolumn convolutional neural network framework, this paper finds that the number of features and the convolution kernel size at different levels of the convolutional neural network are different. In contrast, the proposed method can construct different convolutional neural network models that adapt better to the characteristics of the medical images of interest and thus can better train the resulting heterogeneous multicolumn convolutional neural networks. Finally, using the adaptive sliding window fusion mechanism proposed in this paper, both methods jointly complete the classification task of medical images. Based on the above ideas, this paper proposes a medical classification algorithm based on a weight initialization/sliding window fusion for multilevel convolutional neural networks. The methods proposed in this study were applied to breast mass, brain tumor tissue, and medical image database classification experiments. The results show that the proposed method not only achieves a higher average accuracy than that of traditional machine learning and other deep learning methods but also is more stable and more robust.

Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.

Deep learning models have demonstrated favorable performance on many medical image classification tasks. However, they rely on expensive hand-labeled datasets that are time-consuming to create. In this work, we explore a new supervision source to training deep learning models by using gaze data that is passively and cheaply collected during a clinician’s workflow. We focus on three medical imaging tasks, including classifying chest X-ray scans for pneumothorax and brain MRI slices for metastasis, two of which we curated gaze data for. The gaze data consists of a sequence of fixation locations on the image from an expert trying to identify an abnormality. Hence, the gaze data contains rich information about the image that can be used as a powerful supervision source. We first identify a set of gaze features and show that they indeed contain class-discriminative information. Then, we propose two methods for incorporating gaze features into deep learning pipelines. When no task labels are available, we combine multiple gaze features to extract weak labels and use them as the sole source of supervision (Gaze-WS). When task labels are available, we propose to use the gaze features as auxiliary task labels in a multi-task learning framework (Gaze-MTL). On three medical image classification tasks, our Gaze-WS method without task labels comes within 5 AUROC points (1.7 precision points) of models trained with task labels. With task labels, our Gaze-MTL method can improve performance by 2.4 AUROC points (4 precision points) over multiple baselines.

The transformation model plays a vital role in medical image processing. This paper proposed new two Mixed Transforms models that are the hybrid combination of linear and nonlinear Transformations techniques. The first mixed transform is computed in three steps: calculate 2D discrete cosine transform (DCT) of the image, and applying Arnold Transform (AT) on the DCT coefficients, and applying the discrete Wavelet Transform (DWT) on the result to get which was abbreviated as (CAW). The second mixed transform consists of firstly computing the discrete Fourier transform (DFT), net applying the Arnold Transform (AT), and finally, the computation of discrete Wavelet Transform (DWT) which was abbreviated as (FAW). These transforms have superior directional representations as compared to other multiresolution representations such as DWT or DCT and work as non-adaptive mixed transformations for multi-scale object analysis. Due to their relationship to the wavelet idea, they are finding increasing use in areas like image processing and scientific computing. These transforms are tested in medical image classification task and their performances are compared with that of the traditional transforms. CAW and FAW transforms are used in the feature extraction stage of a classification VGG16 deep learning (DNN) task of Tumor MRI medical image. The numerical findings favor CAW and FAW over the wavelet transform for estimating and classifying pictures. From the results obtained it was shown that the CAW and FAW transform gave e much higher classification rate than that achieved with the traditional transforms, namely DCT, DFT and DWT. Furthermore, this combination leads to a family of directional and multi-transformation bases for image processing.

Medical supervised masked autoencoder: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification

Deep Learning has the potential to renovate healthcare; alternatively, extensive skill is vital to train such models. In medical imaging, a diagnosis with precision and speed is done through Artificial Intelligence (AI), which includes Deep Learning. When compared with healthcare professionals, the Deep Learning models achieved a similar level of diagnostic accuracy. As such, most of the diagnostic technique is systematic, as it has intelligent classification approaches with the computer-aided decision support systems. Deep Learning methods, such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), and Recurrent Neural Network (RNN), support the classification. In recent studies, CNN has been found to be a vital technique for extracting valuable features from a medical dataset of images and may result in higher accuracy. Artificial intelligence (AI) in the form of Deep Learning (DL) and within the field of medical imaging has created remarkable developments in the classification of medical imaging datasets.

Deep learning methods, especially convolutional neural networks, have become more and more popular in medical image classifications. However, training a deep neural network from scratch can be a luxury for many medical image datasets as the process requires a large and well-balanced sample to output satisfactory results. Unlike natural image datasets, medical images are expensive to collect owing to labor and equipment costs. Besides, the class labels in medical image datasets are usually severely imbalanced subject to the availability of patients. Further, aggregating medical images from multiple sources can be challenging due to policy restrictions, privacy concerns, communication costs, and data heterogeneity caused by equipment differences and labeling discrepancies. In this paper, we propose to address these issues with the help of transfer learning and artificial samples created by generative models. Instead of requesting medical images from source data, our method only needs a parsimonious supplement of model parameters pre-trained on the source data. The proposed method preserves the data privacy in the source data and significantly reduces the communication cost. Our study shows transfer learning together with artificial samples can improve the pneumonia classification accuracy on a small but heavily imbalanced chest X-ray image dataset by \(11.53\%\) which performs even better than directly augmenting that source data into the training process.KeywordsDeep learningGenerative modelsMedical image classificationPrivacy preservationTransfer learning

Medical image classification and retrieval systems have been finding extensive use in the areas of image classification according to imaging modalities, body part and diseases. One of the major challenges in the medical classification is the large size images leading to a large number of extracted features which is a burden for the classification algorithm and the resources. In this paper, a novel approach for automatic classification of fundus images is proposed. The method uses image and data pre-processing techniques to improve the performance of machine learning classifiers.<em> </em>Some predominant image mining algorithms such as Classification, Regression Tree (CART), Neural Network, Naive Bayes (NB), Decision Tree (DT) K-Nearest Neighbor. The performance of MCBIR systems using texture and shape features efficient. . The possible outcomes of a two class prediction be represented as True positive (TP), True negative (TN), False Positive (FP) and False Negative (FN).

Medical imaging is an indispensable tool in clinical diagnosis and therapeutic decision-making, encompassing a wide range of modalities such as radiography, ultrasound, CT, and MRI. With the rapid advancement of deep learning technologies, significant progress has been made in medical image analysis. However, existing deep learning methods are often limited by dataset size, which can lead to overfitting, while traditional approaches relying on hand-crafted features lack specificity and fail to fully capture complex pathological information. To address these challenges, we propose RadioGuide-DCN, an innovative radiomics-guided decorrelated classification network. Our method integrates radiomics features as prior information into deep neural networks and employs a feature decorrelation loss mechanism combined with an anti-attention feature fusion module to effectively reduce feature redundancy and enhance the model's capacity to capture both local details and global patterns. Specifically, we utilize a Kolmogorov-Arnold Network (KAN) classifier with learnable activation functions to further boost performance across various medical imaging datasets. Experimental results demonstrate that RadioGuide-DCN achieves an accuracy of 93.63% in BUSI image classification and consistently outperforms conventional radiomics and deep learning methods in multiple medical imaging classification tasks, significantly improving classification accuracy and AUC scores. Our study offers a novel paradigm for integrating deep learning with traditional imaging approaches and holds broad clinical application potential, particularly in tumor detection, image classification, and disease diagnosis.

Medical image classification is an essential task in the fields of computer-aided diagnosis and medical image analysis. In recent years, researchers have made extensive work on medical image classification by computer vision techniques. However, most of the current work is based on deep learning methods, which still suffer from expensive hardware resources, long time consuming and a lot of parameters to be optimized. In this paper, a multi-granularity ensemble algorithm for medical image classification based on broad learning system is proposed, which is an end-to-end lightweight model. On the one hand, the proposed method is designed to address the problem of weak image feature learning ability of broad learning system. The convolution module with fixed weights based on transfer learning is introduced as a feature extractor to extract fusion features of medical images. On the other hand, the multi-granularity ensemble framework is proposed, which learn the fusion features of medical images from fine-grained to coarse-grained respectively, and the prediction results at different granularity levels are integrated by ensemble learning. In this way, the bottom local features can be sufficiently considered, while the global features can also be taken into account. The experimental results show that on the MedMNIST dataset (containing 10 sub-datasets), the proposed method can shorten the training time by tens of times while having similar accuracy to deep convolutional neural networks. On the ChestXRay2017 dataset, the proposed method can achieve an accuracy of 92.5%, and the training time is also significantly better than other methods.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

This review paper examines the current state of lung disease diagnosis based on deep learning (DL) methods. Lung diseases, such as Pneumonia, TB, Covid-19, and lung cancer, are significant causes of morbidity and mortality worldwide. Accurate and timely diagnosis of these diseases is essential for effective treatment and improved patient outcomes. DL methods, which utilize artificial neural networks to extract features from medical images automatically, have shown great promise in improving the accuracy and efficiency of lung disease diagnosis. This review discusses the various DL methods that have been developed for lung disease diagnosis, including convolutional neural networks (CNNs), deep neural networks (DNNs), and generative adversarial networks (GANs). The advantages and limitations of each method are discussed, along with the types of medical imaging techniques used, such as X-ray and computed tomography (CT). In addition, the review discusses the most commonly used performance metrics for evaluating the performance of DL for lung disease diagnosis: the area under the curve (AUC), sensitivity, specificity, F1-score, accuracy, precision, and the receiver operator characteristic curve (ROC). Moreover, the challenges and limitations of using DL for lung disease diagnosis, including the limited availability of annotated data, the variability in imaging techniques and disease presentation, and the interpretability and generalizability of DL models, are highlighted in this paper. Furthermore, strategies to overcome these challenges, such as transfer learning, data augmentation, and explainable AI, are also discussed. The review concludes with a call for further research to address the remaining challenges and realize DL's full potential for improving lung disease diagnosis and treatment.

This paper addresses the task of class-imbalanced medical image classification using complication labels. Medical image classification plays an essential role in improving patient care and reducing the burden on healthcare systems, along with other computer aided diagnosis tasks. However, there are two common obstacles with medical image image classification. One is that there is a high cost of acquiring annotations from doctors/experts. Two is that in many cases there is a differences in the number of incidents that appear per symptom which lead to class-imbalance. The above factors make medical image classification difficult to deal with. Additionally, there are cases where multiple symptoms appear in a mixed manner (complications), such as diabetic complications where both symptoms of infection and ischaemia could appear at the same time, which also could influence the classification result negatively. In this paper, a method that jointly tackles the obstacles mentioned above is presented. First, we conduct extensive experiment with various class-imbalanced learning methods introduced in previous works and also propose a method that improves the baseline class-imbalanced approaches by utilizing complication labels in pre-training. Second, we validate the proposed method with the diabetic foot ulcer dataset introduced in Diabetic Foot Ulcer Challenge 2021.

The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review of recent research that leverage vision transformer (ViT) models for medical image classification across various disciplines. The medical fields of focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart diseases, colon cancer, brain disorders, diabetic retinopathy, skin diseases, kidney diseases, lymph node diseases, and bone analysis. Each work is critically analyzed and interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning techniques, model interpretability, and identified challenges. Our findings suggest that ViT shows promising results in the medical imaging domain, often outperforming traditional convolutional neural networks (CNN). A comprehensive overview is presented in the form of figures and tables summarizing the key findings from each field. This paper provides critical insights into the current state of medical image classification using ViT and highlights potential future directions for this rapidly evolving research area.

In recent years, the integration of machine learning within the field of biological and medical sciences has significantly enhanced our ability to interpret complex clinical data. This advancement has been especially impactful in the domain of neurodegenerative disorders such as Parkinson’s Disease (PD), where early and precise diagnosis remains a persistent challenge. In this study, we propose a novel approach utilizing a Region-based Convolutional Neural Network (R-CNN) to distinguish between healthy individuals and those affected by PD. The method focuses on identifying subtle but meaningful patterns in visual diagnostic tools such as spiral and wave drawings, which are known to reflect motor impairments typical of the disease. Parkinson’s Disease is characterized by the progressive degeneration of motor control, often manifesting in tremors, rigidity, and bradykinesia. While traditional diagnostic techniques depend heavily on the observational skills of neurologists—assessing speech, writing, gait, and hand-drawn spirals—the process can be both time-consuming and subjective. To overcome these limitations, we adopted a deep learning strategy, using Faster R-CNN to directly analyze input patterns without manual feature extraction. This model automates the learning of distinguishing features from a dataset comprising MRI images and spiral drawing samples, enabling accurate classification with minimal human intervention. The system was trained and tested on real biomedical drawing datasets, including spiral and wave patterns from both PD patients and healthy controls. We achieved an accuracy of 96% during training and 86–97% during testing using batch normalization, which stabilized learning and minimized overfitting. This high performance clearly demonstrates the effectiveness of our approach in capturing the fine-grained motor distortions associated with Parkinson’s Disease. The model’s ability to learn from nuanced visual cues reflects its strength in identifying early-stage abnormalities, which are often difficult to detect with conventional tools. Moreover, this research highlights the superiority of the proposed model when compared to existing state-of-the-art methods. The results demonstrated not only robustness in pattern recognition but also the potential for real-world clinical applications. By automating a significant portion of the diagnostic workflow, this approach could reduce diagnostic delays and support neurologists in making more informed decisions. The system has proven effective in its current implementation and will be further validated using a broader, more diverse set of clinical data in future work. Keywords - Deep Learning - R-CNN - Spiral Drawing Analysis - Medical Image Classification - Early Diagnosis.