Hyperparameter Optimization of Deep Neural Network in Multimodality Fused Medical Image Classification for Medical and Industrial IoT

Optimal Deep Neural Network model based multimodality fused medical image classification

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

The process of fusing different images from various imaging modalities into a single, fused image that contains a wealth of information and improves the usability of medical images in real-world applications is known as medical image fusion. The most useful features from data can be automatically extracted by deep learning models. In the recent past, the field of image fusion has been preparing to introduce a deep learning model. In this work we can achieve the multi-Focus medical image fusion by hybrid deep learning models. Here the relevant health care data are collected from database (CT & MRI brain images). Following the input images are pre-processed using sliding window and the abnormal data is eliminated using distribution map method. Further the proposed work comprises 3 steps, 1) the proposed method is used to extract the features from the input image using the modified Tetrolet transform (MMT), which uses a brain image as an input image. This model is capable of identifying anomalous trends in time series data and automatically deriving from the input data characteristics that characterise the system state.2) Propose a novel hybrid model based on CNN with Bi-LSTM (Bi-directional Short Term Memory) multi-focus image fusion method to overcome the difficulty faced by the existing fusion methods. 3) This hybrid model are used to predict the brain tumor present in the fused image. Finally, experimental results are evaluated using a variety of performance measures. From the results, we can see that our suggested model contributes to an increase in predictive performance while also lowering the complexity in terms of storage and processing time.

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.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

The multi‐modal medical image fusion (M3IF) combines the required and important information from different medical imaging modalities (computed tomography [CT], magnetic resonance imaging (MRI), positron emission tomography [PET], and single photon emission computed tomography [SPECT]) to provide single informative image. M3IF provides enhanced patient diagnosis, and precise treatment planning. This paper proposes a hybrid M3IF where input medical images are decomposed using stationary wavelet transform (SWT) into low‐frequency components (LFCs) and high‐frequency components (HFCs). The LFCs and HFCs are fused using energy‐ and contrast‐based metrics. And later reconstruction is performed using inverse SWT (ISWT). The total variation minimization (TVM) using Chambolle's algorithm is applied as a post‐refinement operation to reduce noise and preserves the fine details. In this paper, the proposed methodology is termed as M3IF‐(SWT‐TVC), Here, the acronym TVC is the combination of TVM using Chambolle's algorithm. TVM refinement process is an iterative approach, with the fusion outcomes of M3IF‐(SWT‐TVC) assessed over a predefined 100 iterations. The TVM, and SWT are blended to balance smoothness and structural details. The final fusion results obtained through M3IF‐(SWT‐TVC) are evaluated against several prominent non‐traditional methods. Based on both visual quality and quantitative metric analysis, it is observed that M3IF‐(SWT‐TVC) outperforms all the methods used for comparison.

As a typical application of the Internet of Things (IoT), the Industrial IoT (IIoT) connects all the related IoT sensing and actuating devices ubiquitously so that the monitoring and control of numerous industrial systems can be realized. Deep learning, as one viable way to carry out big-data-driven modeling and analysis, could be integrated in IIoT systems to aid the automation and intelligence of IIoT systems. As deep learning requires large computation power, it is commonly deployed in cloud servers. Thus, the data collected by IoT devices must be transmitted to the cloud for training process, contributing to network congestion and affecting the IoT network performance as well as the supported applications. To address this issue, in this article, we leverage the fog/edge computing paradigm and propose an edge computing-based deep learning model, which utilizes edge computing to migrate the deep learning process from cloud servers to edge nodes, reducing data transmission demands in the IIoT network and mitigating network congestion. Since edge nodes have limited computation ability compared to servers, we design a mechanism to optimize the deep learning model so that its requirements for computational power can be reduced. To evaluate our proposed solution, we design a testbed implemented in the Google cloud and deploy the proposed convolutional neural network (CNN) model, utilizing a real-world IIoT data set to evaluate our approach. 1 Our experimental results confirm the effectiveness of our approach, which cannot only reduce the network traffic overhead for IIoT but also maintain the classification accuracy in comparison with several baseline schemes. 1 Certain commercial equipment, instruments, or materials are identified in this article in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach.

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 advancements in deep learning (DL) and transfer learning (TL) have transformed artificial intelligence, particularly in image classification. This research examines the theoretical foundations of DL and TL, focusing on their applications in medical image classification, specifically distinguishing between COVID-19, viral pneumonia, and normal lung conditions. By leveraging GPU-enabled high-performance computing and large labeled datasets, DL models particularly Convolutional Neural Networks (CNNs) such as ResNet50 and VGG16 have achieved superior accuracy compared to traditional machine learning methods. This study explores feature extraction using pre-trained models, the implementation of classifiers like Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and the integration of multi-view learning techniques, such as early fusion. The results demonstrate the effectiveness of DL and TL in improving classification performance, highlighting their significant potential to advance global healthcare diagnostics.

Metaheuristics based COVID-19 detection using medical images: A review

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.

Nowadays, there are a lot of medical images and their numbers are increasing day by day. These medical images are stored in the large database. To minimize the redundancy and optimize the storage capacity of images, medical image fusion is used. The main aim of medical image fusion is to combine complementary information from multiple imaging modalities (e.g. CT, MRI, PET, etc.) of the same scene. After performing medical image fusion, the resultant image is more informative and suitable for patient diagnosis. There are some fusion techniques which are described in this chapter to obtain fused image. This chapter presents two approaches to image fusion, namely spatial domain Fusion technique and transforms domain Fusion technique. This chapter describes Techniques such as Principal Component Analysis which is spatial domain technique and Discrete Wavelet Transform and Stationary Wavelet Transform which are Transform domain techniques. Performance metrics are implemented to evaluate the performance of image fusion algorithm.