An image quality enhancement scheme employing adolescent identity search algorithm in the NSST domain for multimodal medical image fusion

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

EMFusion: An unsupervised enhanced medical image fusion network

A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

The fusion of multi-modal medical images makes a significant contribution to clinical diagnosis and analysis because it allows diagnostic imaging practitioners to make a more accurate diagnosis. According to our current knowledge, there are some limitations of current medical image fusion approaches. The first limitation is that the use of a weighted average rule for fusing low-frequency components. This limitation leads to a decrease in the intensity of the brightness of the fused image. The second limitation is that the utilizing of fusion rules for high-frequency components is not really optimal. This is likely to result in the loss of detailed information in the fused image. In this paper, a novel approach, including two algorithms, is proposed to address the above-mentioned limitations. The first algorithm is based on the Grasshopper optimization algorithm (GOA) to find optimal parameters with the aim of fusing low-frequency components. This allows the fused image to have good contrast. The second algorithm is based on the Kirsch compass operator to create an efficient rule for the fusion of high-frequency components. This allows the fused image to significantly preserve details transferred from input images. Experimental results show that the proposed approach not only effective in enhancing significantly the contrast of the fused image but also preserving edge information carried from input images to the composite image.

A novel approach based on Grasshopper optimization algorithm for medical image fusion

Multi-modal Medical Image Fusion based on Two-scale Image Decomposition and Sparse Representation

Clinical diagnosis has increased marvelous significance in current day healthcare systems. This article proposes a brain tumor detection method using edge detection based fuzzy logic and U-NET Convolutional Neural Network (CNN) classification method. The proposed tumor segmentation system is based on image enhancement, fuzzy logic based edge detection, and classification. The input images are pre-processed using the contrast enhancement and fuzzy logic-based edge detection method is applied to identify the edge in the source images and dual tree-complex wavelet transform (DTCWT) is used at different scale levels. The features are calculated from the decayed sub-band images and these features are then categorized using U-NET CNN classification which recognizes the meningioma and non-meningioma brain images. The proposed method is evaluated using accuracy, sensitivity, specificity, and dice coefficient index. Simulation study demonstrates that the proposed technique achieves better performance, both visually and quantitatively in comparison with other approaches.

An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification

Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based on magnetic resonance imaging. For computer-aided diagnosis methods to assist radiologists in the proper detection of brain tumors, medical imagery must be detected, segmented, and classified. Manual brain tumor detection is a monotonous and error-prone procedure for radiologists; hence, it is very important to implement an automated method. As a result, the precise brain tumor detection and classification method is presented. Materials and Methods: The proposed method has five steps. In the first step, a linear contrast stretching is used to determine the edges in the source image. In the second step, a custom 17-layered deep neural network architecture is developed for the segmentation of brain tumors. In the third step, a modified MobileNetV2 architecture is used for feature extraction and is trained using transfer learning. In the fourth step, an entropy-based controlled method was used along with a multiclass support vector machine (M-SVM) for the best features selection. In the final step, M-SVM is used for brain tumor classification, which identifies the meningioma, glioma and pituitary images. Results: The proposed method was demonstrated on BraTS 2018 and Figshare datasets. Experimental study shows that the proposed brain tumor detection and classification method outperforms other methods both visually and quantitatively, obtaining an accuracy of 97.47% and 98.92%, respectively. Finally, we adopt the eXplainable Artificial Intelligence (XAI) method to explain the result. Conclusions: Our proposed approach for brain tumor detection and classification has outperformed prior methods. These findings demonstrate that the proposed approach obtained higher performance in terms of both visually and enhanced quantitative evaluation with improved accuracy.

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Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.

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Sarmad Maqsood

Papers

Multi-modal Medical Image Fusion based on Two-scale Image Decomposition and Sparse Representation

An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages