Optimal Deep Neural Network model based multimodality fused medical image classification

Industrial Internet of Things (IIoT) refers to the extension and utilization of the Internet of Things (IoT) in industrial sectors and applications. Medical image fusion and classification has been utilized to get valuable significant multimodality medical image data. Recently, deep learning methods offer an effective way to design an end-to-end model that can determine the final classification labels. This chapter introduces a Multimodality Image Fusion Classification (MMIFC) by the incorporation of image fusion, feature extraction, and classification techniques. Initially, the input medical images were fused by the use of optimal shearlet transform where the coefficients of shearlets are optimized by the use of Enhanced Monarch Butterfly Optimization (EMBO) algorithm. In the next stage, the fused images were classified based on the feature extractor and deep learning model to check the test image is malignant and benign. To further optimize the performance of the deep learning model, hyperparameter tuning process takes place by the use of Bayesian optimization model to optimize the weights of structure. Finally, this ODNN model classifies the MMFI as class 1 or 0. A detailed simulation result takes place to ensure the effective performance of the proposed method. From the experimental results, the proposed strategy accomplishes better fusion rate and classification results compared with other supervised and non-supervised learning techniques.

Multimodal medical image fusion plays an important role in clinical applications. However, multimodal medical image fusion methods ignore the feature dependence among modals, and the feature fusion ability with different granularity is not strong. A Long-Range Correlation-Guided Dual-Encoder Fusion Network for Medical Images is proposed in this paper. The main innovations of this paper are as follows: Firstly, A Cross-dimension Multi-scale Feature Extraction Module (CMFEM) is designed in the encoder, by extracting multi-scale features and aggregating coarse-to-fine features, the model realizes fine-grained feature enhancement in different modalities. Secondly, a Long-range Correlation Fusion Module (LCFM) is designed, by calculating the long-range correlation coefficient between local features and global features, the same granularity features are fused by the long-range correlation fusion module. long-range dependencies between modalities are captured by the model, and different granularity features are aggregated. Finally, this paper is validated on clinical multimodal lung medical image dataset and brain medical data dataset. On the lung medical image dataset, IE, AG, {textbf {Q}}^{{textbf {AB/F}}}, and EI metrics are improved by 4.53%, 4.10%, 6.19%, and 6.62% respectively. On the brain medical image dataset, SF, VIF, and {textbf {Q}}^{{textbf {AB/F}}} metrics are improved by 3.88%, 15.71%, and 7.99% respectively. This model realizes better fusion performance, which plays an important role in the fusion of multimodal medical images.

Background: During the two past decades, medical image fusion has become an essential part ofmodern medicine due to the availability of numerous imaging modalities (e.g., MRI, CT, SPECT,etc.). This paper presents a new medical image fusion algorithm based on PCA and DTCWT,which uses different fusion rules to obtain a new image containing more information than any ofthe input images.Methods: A new image fusion algorithm improves the visual quality of the fused image, based onfeature selection and Principal Component Analysis (PCA) in the Dual-Tree Complex WaveletTransform (DTCWT) domain. It is called Feature Selection with Principal Component Analysisand Dual-Tree Complex Wavelet Transform (FSPCA-DTCWT). Using different fusion rules in asingle algorithm result in correctly reconstructed image (fused image), this combination willproduce a new technique, which employs the advantages of each method separately. The DTCWTpresents good directionality since it considers the edge information in six directions and providesapproximate shift invariant. The main goal of PCA is to extract the most significant characteristics(represented by the wavelet coefficients) in order to improve the spatial resolution. The proposedalgorithm fuses the detailed wavelet coefficients of input images using features selection rule.Results: Several experiments have been conducted over different sets of multimodal medicalimages such as CT/MRI, MRA/T1-MRI. However, due to pages-limit on a paper, only results ofthree sets have been presented. The FSPCA-DTCWT algorithm is compared to recent fusionmethods presented in the literature (eight methods) in terms of visual quality and quantitativelyusing well-known fusion performance metrics (five metrics). Results showed that the proposedalgorithm outperforms the existing ones regarding visual and quantitative evaluations.Conclusion: This paper focuses on medical image fusion of different modalities. A novel imagefusion algorithm based on DTCWT to merge multimodal medical images has been proposed.Experiments have been performed using two different sets of multimodal medical images. Theresults show that the proposed fusion method significantly outperforms the recent fusiontechniques reported in the literature.

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.

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information. The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details. NSCT is utilized to further decompose these images into their low and high-frequency coefficients. These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve (PCA/KL) based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results. An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrast-enhanced fusion coefficients. Finally, an inverse NSCT is applied to each coefficient to produce the final fusion result. Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health. Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis.

Multi-modal medical image fusion by Laplacian pyramid and adaptive sparse representation

Non-negative subspace feature representation for few-shot learning in medical imaging

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image generation to support AI training. GANs are especially useful for medical imaging-related tasks where training datasets are usually limited in size and heavily imbalanced against the diseased class. We present a systematic review, following the PRISMA guidelines, of recent GAN architectures used for medical image analysis to help the readers in making an informed decision before employing GANs in developing medical image classification and segmentation models. We have extracted 54 papers that highlight the capabilities and application of GANs in medical imaging from January 2015 to August 2020 and inclusion criteria for meta-analysis. Our results show four main architectures of GAN that are used for segmentation or classification in medical imaging. We provide a comprehensive overview of recent trends in the application of GANs in clinical diagnosis through medical image segmentation and classification and ultimately share experiences for task-based GAN implementations.

Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on Gradient Orientation Bracketed pairs. The source medical images are first transformed by Image separation (DOF) followed by combining background and foreground parts frequency components. The Standard Dynamic Range (SDR) device traces image details like contrasts and gradient filter direction in a series of SDR images with different levels. The image overall luminance levels are calculated with the image series, which maximizes the observable distinctions, and then the gradients embedded in these images. The fusion algorithm techniques are used for fusion of images based on Luminance and gradient level. This is done in a multi-resolution of illumination variation in the sequence. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images.

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.

Deep Learning has radically transformed the field of Medical Image Analysis by surpassing human performance in several challenging classification tasks and thus has widespread applications in industry and academia. In this work, we employ a Deep Convolutional Neural Network (DCNN) that uses Transfer Learning and is trained using the Open-access Medical Image Repositories and OpenI repository. We use DCNN to assist in a crowdsourced Big Data platform by conducting two crucial classification tasks: (1) Interclass classification of the type of the medical image—MRI, Ultrasound or X-Ray; (2) Intraclass classification of the X-Ray images into chest or abdomen X-Rays. Our approach automates the classification process of medical images, to provide certified medical experts with medical images of individuals on a crowdsourced platform, along with similar medical images of the suspected medical condition for validation, in order to enhance diagnostic effectiveness. In this work, we focus primarily on the classification tasks using DCNN; the elucidation of the privacy-preserving architecture of our cloud-based crowdsourcing platform and classification using conventional clustering approaches in the absence of large training images is deferred to future work.KeywordsBig dataDeep learningDCNNCrowdsourcingMedical image classificationObject detection

Medical image fusion based on convolutional neural networks and non-subsampled contourlet transform

Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.

Medical image fusion is very important for the diagnosis and treatment of diseases. In recent years, there have been a number of different multi-modal medical image fusion algorithms that can provide delicate contexts for disease diagnosis more clearly and more conveniently. Recently, nuclear norm minimization and deep learning have been used effectively in image processing. A multi-modality medical image fusion method using a rolling guidance filter (RGF) with a convolutional neural network (CNN) based feature mapping and nuclear norm minimization (NNM) is proposed. At first, we decompose medical images to base layer components and detail layer components by using RGF. In the next step, we get the basic fused image through the pretrained CNN model. The CNN model with pre-training is used to obtain the significant characteristics of the base layer components. And we can compute the activity level measurement from the regional energy of CNN-based fusion maps. Then, a detail fused image is gained by NNM. That is, we use NNM to fuse the detail layer components. At last, the basic and detail fused images are integrated into the fused result. From the comparison with the most advanced fusion algorithms, the results of experiments indicate that this fusion algorithm has the best effect in visual evaluation and objective standard. The fusion algorithm using RGF and CNN-based feature mapping, combined with NNM, can improve fusion effects and suppress artifacts and blocking effects in the fused results.

With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies-pulse-coupled neural network based on the structure tensor and maximum based-are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method's outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.