Medical Image Classification Research Articles

Ensemble ResDenseNet: Alzheimer’s disease staging from brain MRI using deep weighted ensemble transfer learning

Alzheimer’s disease (AD) is a persistent neurological disorder with almost no current cure, although available medications can mitigate its progression. The timely identification and staging of AD is pivotal in impeding and managing its advancement. This study aims to develop a comprehensive framework for the early detection of AD and the classification of medical images across four AD stages. We have proposed a weighted ensemble deep transfer learning framework using two pretrained CNN architectures, namely ResNet152V2 and DenseNet201. Additionally, gradient-weighted class activation mapping (Grad-CAM) is utilized to enhance interpretability by capturing gradients associated with AD in brain MRI images, propagating them to the final convolutional layer. This technique narrows the interpretability gap in deep learning models, enhancing accessibility and understanding of their decision-making processes, particularly in the context of AD diagnosis. Two different MRI datasets have been used to train and evaluate the performances of the proposed framework focusing on generalization capability with diverse data. The performances of the framework are analyzed meticulously under different case studies. The experimental results demonstrate that the weighted ensemble architecture exhibits superior performance characteristics over base models showing accuracy of 98%, which is compatible with related state of the art algorithms.

Res-MGCA-SE: a lightweight convolutional neural network based on vision transformer for medical image classification

Res-MGCA-SE: a lightweight convolutional neural network based on vision transformer for medical image classification

Multi-branch CNN and grouping cascade attention for medical image classification

Visual Transformers(ViT) have made remarkable achievements in the field of medical image analysis. However, ViT-based methods have poor classification results on some small-scale medical image classification datasets. Meanwhile, many ViT-based models sacrifice computational cost for superior performance, which is a great challenge in practical clinical applications. In this paper, we propose an efficient medical image classification network based on an alternating mixture of CNN and Transformer tandem, which is called Eff-CTNet. Specifically, the existing ViT-based method still mainly relies on multi-head self-attention (MHSA). Among them, the attention maps of MHSA are highly similar, which leads to computational redundancy. Therefore, we propose a group cascade attention (GCA) module to split the feature maps, which are provided to different attention heads to further improves the diversity of attention and reduce the computational cost. In addition, we propose an efficient CNN (EC) module to enhance the ability of the model and extract the local detail information in medical images. Finally, we connect them and design an efficient hybrid medical image classification network, namely Eff-CTNet. Extensive experimental results show that our Eff-CTNet achieves advanced classification performance with less computational cost on three public medical image classification datasets.

Medical Tumor Image Classification Based on Few-Shot Learning.

As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images that contribute to improve the accuracy of machine learning algorithm, especially for deep learning in CAD, are difficult to collect. Therefore, in this work, an improved few-shot learning method is proposed for medical image recognition. In addition, to make full use of the limited feature information in one or more samples, a feature fusion strategy is involved in our model. On the dataset of BreakHis and skin lesions, the experimental results show that our model achieved the classification accuracy of 91.22% and 71.20% respectively when only 10 labeled samples are given, which is superior to other state-of-the-art methods.

Feature Selection and Classification with the Annealing Optimization Deep Learning for the Multi-Modal Image Processing

This paper investigates and compares various feature selection algorithms within the context of image processing across multiple datasets. The study evaluates Seahorse Annealing Optimization for Feature Selection (SAO-FS), Genetic Algorithms (GA), CNN + Feature Fusion Network, and Lasso Regression on distinct image datasets—medical images, satellite images, MRI scans, and microscopy images. Performance metrics including accuracy, precision, recall, and computational time are analyzed to assess the efficacy of each algorithm in optimizing feature subsets for classification tasks. SAO-FS demonstrates superior performance in medical image classification with an accuracy of 92.5%, showcasing its ability to achieve high precision and recall rates critical for medical diagnostics. GA proves effective for satellite imagery with an accuracy of 87.3%, while the CNN + Feature Fusion Network excels in MRI scans with 89.8% accuracy. Lasso Regression, though slightly less accurate at 85.6%, efficiently selects features for microscopy images within a shorter computational time. These findings highlight the strengths and trade-offs of each algorithm across different image processing domains, providing insights for selecting appropriate feature selection methods tailored to specific imaging applications.

Artificial intelligence in pharmacy an overview

Intelligent technologies will someday replace or enhance human talent in many areas. Artificial intelligence is the intelligence exhibited by robots or software. It falls under the field of computer science. Computer scientists are becoming more and more interested in studying artificial intelligence because it has significantly enhanced human lives in a variety of ways. Over the past 20 years, artificial intelligence has led to a major boost in the performance of industrial and service systems. Expert systems are a rapidly developing technology that are the result of research into artificial intelligence. Artificial intelligence applications known as expert systems are widely used today to solve complex problems in a range of fields, including science, engineering, business, medicine, and weather forecasting. Artificial intelligence technology have led to improvements in quality and efficiency in certain sectors. This page gives a summary of the technology and some possible uses for it. This paper will also look at the current applications of artificial intelligence technologies in the fields of computer games, accounting databases, hospital inpatient care, medical image classification, network intrusion, Power System Stabilization (PSS) design, and network security against hackers.

Leukemia Detection using Machine Learning Algorithms: Current trends and future directions

The process of medical diagnosis of leukemia cases is a complex process and requires diligent efforts. It is done by examining samples under a microscope and distinguishing the number, shape, size, and morphological features of white blood cells. This process takes a long time to predict leukemia. The professional skills and experience of the pathologist may also influence this procedure as a large number of overlapping structures and conditions, distractions, fatigue, and limitations in the human visual system can lead to an inappropriate diagnosis. Machine and deep learning approaches have been found to be effective strategies for enhancing the precision and efficiency of diagnosis and classification of medical images, including those of microscopic blood cells. In this paper, we will review the recent studies of leukemia detection and/or classification in the period of (2015 - 2023) in machine and deep learning and the combining of them. The review of these schemes is carried out in a systematic manner. In order to achieve the desired objective, segmentation schemes can be classified into four main categories: supervised machine learning techniques, unsupervised machine learning techniques, deep learning approaches, and traditional image processing techniques. Classification approaches can be categorized into three main groups: classical machine learning, deep learning, and a combination of both. CNN-based categorization systems can be further classified into three categories: conventional CNN, transfer learning, and additional developments in CNN. This paper also provides a concise analysis of these strategies and their significance in the categorization of leukemia. Ultimately, a comprehensive examination is conducted to elucidate the current state of research in this particular domain.

Leukemia Detection using Machine Learning Algorithms: Current trends and future directions

The process of medical diagnosis of leukemia cases is a complex process and requires diligent efforts. It is done by examining samples under a microscope and distinguishing the number, shape, size, and morphological features of white blood cells. This process takes a long time to predict leukemia. The professional skills and experience of the pathologist may also influence this procedure as a large number of overlapping structures and conditions, distractions, fatigue, and limitations in the human visual system can lead to an inappropriate diagnosis. Machine and deep learning approaches have been found to be effective strategies for enhancing the precision and efficiency of diagnosis and classification of medical images, including those of microscopic blood cells. In this paper, we will review the recent studies of leukemia detection and/or classification in the period of (2015 - 2023) in machine and deep learning and the combining of them. The review of these schemes is carried out in a systematic manner. In order to achieve the desired objective, segmentation schemes can be classified into four main categories: supervised machine learning techniques, unsupervised machine learning techniques, deep learning approaches, and traditional image processing techniques. Classification approaches can be categorized into three main groups: classical machine learning, deep learning, and a combination of both. CNN-based categorization systems can be further classified into three categories: conventional CNN, transfer learning, and additional developments in CNN. This paper also provides a concise analysis of these strategies and their significance in the categorization of leukemia. Ultimately, a comprehensive examination is conducted to elucidate the current state of research in this particular domain.

Classification of recurrent depression using brain CT images through feature fusion

ObjectiveThe objective of the study is to address the issues of high workload and low diagnostic efficiency in clinical medicine by employing computer-aided diagnostic analysis methods. The focus is on the identification of depressed patients using medical image data, particularly brain CT images. MethodsThe study proposes a CT image classification method for identifying depression disorder based on deep learning theory. Key methods include migration learning and feature fusion. Preprocessing and data enhancement techniques are utilized to filter out unnecessary features. An attention module is employed to better extract deep feature information. Additionally, a binary classification focus loss function is applied to address the unbalanced distribution of the dataset. ResultsExperimental results indicate that the proposed method achieves an image classification accuracy of 97.79%. Compared to a single model, there is an increase in accuracy by 2.61% and 1.81%, respectively. This improvement effectively enhances the classification accuracy of brain CT images with depressive disorders. The classification model also demonstrates better generalization performance. ConclusionThe study concludes that the proposed CT image classification method, incorporating migration learning, feature fusion, preprocessing, and attention modules, significantly improves the accuracy and classification speed of identifying depressive disorders in brain CT images. The model's generalization performance is highlighted, providing effective support for the enhancement of medical image classification accuracy.

Deep learning for MRI lesion segmentation in rectal cancer.

Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients' medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors' ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Radiological image analysis using effective channel extension and fusion network based on COVID CT images

Attention mechanisms demonstrate significant capabilities in improving the performance of COVID CT image classification networks. Existing strategies often employ global pooling to acquire feature vectors, overlooking local feature information. Complex attention modules are then applied to enhance network performance, inevitably leading to increased model complexity. To address these issues, this paper introduces an Effective Channel Expansion and Fusion (ECEF) module specifically designed to enhance the performance of medical COVID CT image classification. The module represents a simple yet effective convolutional neural network module. In the channel direction of the given feature map, the module performs segmentation, conducts global pooling operations separately on the segmented and overall feature maps to obtain corresponding attention vectors, and merges them to produce high-dimensional attention vectors. Channel attention feature vectors are derived along the two pooling operation directions and dynamically fused. The resulting attention vectors are multiplied by the input feature map for adaptive feature refinement. Through these operations, the ECEF module adeptly captures local features in COVID CT images, enhancing the classification performance of medical COVID CT images. As a lightweight and versatile module, ECEF seamlessly integrates into any CNN architecture with negligible overhead. It can undergo end-to-end training alongside basic CNNs. ResNet is selected as the backbone network for empirical studies conducted on COVID CT images, including the COVID-CT dataset. Experimental results indicate that, compared to current advanced channel attention mechanisms, the ECEF module exhibits superior performance in COVID CT image classification tasks. The successful application of the ECEF module establishes a foundation for its widespread use in areas such as COVID CT medical image segmentation and object detection.

An overview of computer—aided medical image classification

An overview of computer—aided medical image classification

A Review on Machine Learning and Deep Learning Methods on Medical Image Classification

Medical image classification, a critical component in medical diagnostics, has significantly advanced through the integration of machine learning (ML) and deep learning (DL) techniques. This review comprehensively explores the evolution, methodologies, and applications of ML and DL in medical image classification. Traditional ML methods, including support vector machines and decision trees, have provided a foundation for early advancements by utilizing handcrafted features. However, the advent of DL, particularly convolutional neural networks (CNNs), has revolutionized the field by enabling automatic feature extraction and achieving superior performance. This review examines various DL architectures, such as ResNet, VGG, and Inception, highlighting their contributions to tasks like tumor detection, organ segmentation, and disease classification. Furthermore, it addresses challenges like data scarcity, interpretability, and computational demands, discussing potential solutions like data augmentation, transfer learning, and model optimization. The review also considers the ethical implications and the need for robust validation to ensure clinical applicability. Through a comparative analysis of existing studies, this review underscores the transformative impact of ML and DL on medical imaging, emphasizing the continuous need for innovation and interdisciplinary collaboration to enhance diagnostic accuracy and patient outcomes.

A hyperdimensional framework: Unveiling the interplay of RBP and GSN within CNNs for ultra-precise brain tumor classification

This study presents the RBP-CNN model, a convolutional neural network specifically designed for the precise classification of brain tumors in medical imaging. Conventional methods often encounter difficulties in extracting image noise and texture features, which has led to the incorporation of regional binary patterns (RBP) and Gray Standard Normalization (GSN) preprocessing techniques in CNN. The research addresses fundamental inquiries regarding the impact of the model on accuracy, false classifications, and efficiency. The novelty of RBP-CNN lies in its distinctive approach to extracting texture features, which involves optimizing pixel values through GSN preprocessing and generating regional binary patterns based on integral images. The objective of this research is to bridge a critical gap by providing a more accurate and efficient model for classifying brain tumors. The key findings reveal the exceptional performance of RBP-CNN, achieving a classification accuracy of 96% with a reduced false classification ratio of 7% across a dataset of 3000 samples. Comparative analyses position RBP-CNN as superior to alternative models in terms of accuracy, false classification rates, and efficiency. The structural insights and hyperparameter values of the model, as well as its application to the FigShare dataset, demonstrate its robustness and scalability. RBP-CNN emerges as an innovative and effective solution, advancing the field of medical image categorization. The findings of this study contribute a novel methodology, paving the way for future exploration in hyperspectral image applications and positioning RBP-CNN as a potential state-of-the-art tool for medical image analysis.

Instance-level medical image classification for text-based retrieval in a medical data integration center

A medical data integration center integrates a large volume of medical images from clinical departments, including X-rays, CT scans, and MRI scans. Ideally, all images should be indexed appropriately with standard clinical terms. However, some images have incorrect or missing annotations, which creates challenges in searching and integrating data centrally. To address this issue, accurate and meaningful descriptors are needed for indexing fields, enabling users to efficiently search for desired images and integrate them with international standards.This paper aims to provide concise annotation for missing or incorrectly indexed fields, incorporating essential instance-level information such as radiology modalities (e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g., lateral) using a Deep Learning classification model - ResNet50. To demonstrate the capabilities of our algorithm in generating annotations for indexing fields, we conducted three experiments using two open-source datasets, the ROCO dataset, and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels. While the outcomes of these experiments are satisfactory (Precision of >75%) for less critical tasks and serve as a valuable testing ground for image retrieval, they also underscore the need for further exploration of potential challenges. This essay elaborates on the identified issues and presents well-founded recommendations for refining and advancing our proposed approach.

Class imbalance on medical image classification: towards better evaluation practices for discrimination and calibration performance.

This work aims to assess standard evaluation practices used by the research community for evaluating medical imaging classifiers, with a specific focus on the implications of class imbalance. The analysis is performed on chest X-rays as a case study and encompasses a comprehensive model performance definition, considering both discriminative capabilities and model calibration. We conduct a concise literature review to examine prevailing scientific practices used when evaluating X-ray classifiers. Then, we perform a systematic experiment on two major chest X-ray datasets to showcase a didactic example of the behavior of several performance metrics under different class ratios and highlight how widely adopted metrics can conceal performance in the minority class. Our literature study confirms that: (1) even when dealing with highly imbalanced datasets, the community tends to use metrics that are dominated by the majority class; and (2) it is still uncommon to include calibration studies for chest X-ray classifiers, albeit its importance in the context of healthcare. Moreover, our systematic experiments confirm that current evaluation practices may not reflect model performance in real clinical scenarios and suggest complementary metrics to better reflect the performance of the system in such scenarios. Our analysis underscores the need for enhanced evaluation practices, particularly in the context of class-imbalanced chest X-ray classifiers. We recommend the inclusion of complementary metrics such as the area under the precision-recall curve (AUC-PR), adjusted AUC-PR, and balanced Brier score, to offer a more accurate depiction of system performance in real clinical scenarios, considering metrics that reflect both, discrimination and calibration performance. This study underscores the critical need for refined evaluation metrics in medical imaging classifiers, emphasizing that prevalent metrics may mask poor performance in minority classes, potentially impacting clinical diagnoses and healthcare outcomes. Common scientific practices in papers dealing with X-ray computer-assisted diagnosis (CAD) systems may be misleading. We highlight limitations in reporting of evaluation metrics for X-ray CAD systems in highly imbalanced scenarios. We propose adopting alternative metrics based on experimental evaluation on large-scale datasets.

A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge. In the RBPNN pattern aggregation layer, the outputs of RBPN are selectively summed according to the category of the kernel center, that is, the subcategory features are combined into category features, and finally the image classification is implemented based on Softmax. The functional module of the proposed method is designed specifically for image characteristics, which can highlight the significance of local and structural features of the image, form a non-convex decision-making area, and reduce the requirements for the completeness of the sample set. Applying the proposed method to medical image classification, experiments were conducted based on the brain tumor MRI image classification public dataset and the actual cardiac ultrasound image dataset, and the accuracy rate reached 85.82% and 83.92% respectively. Compared with the three mainstream image classification models, the performance indicators of this method have been significantly improved.

HTC-retina: A hybrid retinal diseases classification model using transformer-Convolutional Neural Network from optical coherence tomography images

Retinal diseases are among nowadays major public health issues, deservedly needing advanced computer-aided diagnosis. We propose a hybrid model for multi label classification, whereby seven retinal diseases are automatically classified from Optical Coherence Tomography (OCT) images. We show that, by combining the strengths of Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), we can produce a more powerful type of model for medical image classification, especially when considering local lesion information such as retinal diseases. CNNs are indeed proved to be efficient at parameter utilization and provide the ability to extract local features and multi-scale feature maps through convolutional operations. On the other hand, ViT’s self-attention procedure allows processing long-range and global dependencies within an image. The paper clearly shows that the hybridization of these complementary capabilities (CNNs-ViTs) presents a high image processing potential that is more robust and efficient. The proposed model adopts a hierarchical CNN module called Convolutional Patch and Token Embedding (CPTE) instead of employing a direct tokenization approach using the raw input OCT image in the transformer. The CPTE module’s role is to incorporate an inductive bias, to reduce the reliance on large-scale datasets, and to address the low-level feature extraction challenges of the ViT. In addition, considering the importance of local lesion information in OCT images, the model relies on a parallel module called Residual Depthwise-Pointwise ConvNet (RDP-ConvNet) for extracting high-level features. RDP-ConvNet utilizes depthwise and pointwise convolution layers within a residual network architecture. The overall performance of the HTC-Retina model was evaluated on three datasets: the OCT-2017, OCT-C8, and OCT-2014 ; outperforming previous established models, achieving accuracy rates of 99.40%, 97.00%, and 99.77%, respectively ; and sensitivity rates of 99.41%, 97.00%, and 99.77%, respectively. Notably, the model showed high performance while maintaining computational efficiency.

Detecting tumors in medical images using segmentation and feature extraction techniques

Tumors rank among the most prevalent and lethal diseases worldwide, emphasizing the paramount importance of early detection in mitigating mortality rates. There has been a tremendous growth in medical image segmentation and classification for diagnosing cancer. This led to the various developments in medical research that help in diagnosing types of cancers and other human medical problems. This work focuses on performing effective segmentation procedures and feature extraction by a proposed hybrid intelligent technique for detecting tumors in the medical images. Additionally, the research addresses the design aspects of feature extraction, image classification, and texture features. These features are reduced by employing feature selection. Finally, experiments were conducted to compare tumor detection in medical images using the proposed algorithm with other existing algorithms. The results demonstrated promising achievements in terms of their evaluation metrics.