A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images.

Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by 1.08%, 4.13%, and classification by 1.16%, 2.34%, respectively than the methods available in the literature.

An Improved Multimodal Medical Image Fusion Approach Using Intuitionistic Fuzzy Set and Intuitionistic Fuzzy Cross-Correlation.

Multimodal medical image fusion (MMIF) is the process of merging different modalities of medical images into a single output image (fused image) with a significant quantity of information to improve clinical applicability. It enables a better diagnosis and makes the diagnostic process easier. In medical image fusion (MIF), an intuitionistic fuzzy set (IFS) plays a role in enhancing the quality of the image, which is useful for medical diagnosis. In this article, a new approach to intuitionistic fuzzy set-based MMIF has been proposed. Initially, the input medical images are fuzzified and then create intuitionistic fuzzy images (IFIs). Intuitionistic fuzzy entropy plays a major role in calculating the optimal value for three degrees, namely, membership, non-membership, and hesitation. After that, the IFIs are decomposed into small blocks and then perform the fusion rule. Finally, the enhanced fused image can be obtained by the defuzzification process. The proposed method is tested on various medical image datasets in terms of subjective and objective analysis. The proposed algorithm provides a better-quality fused image and is superior to other existing methods such as PCA, DWTPCA, contourlet transform (CONT), DWT with fuzzy logic, Sugeno's intuitionistic fuzzy set, Chaira's intuitionistic fuzzy set, and PC-NSCT. The assessment of the fused image is evaluated with various performance metrics such as average pixel intensity (API), standard deviation (SD), average gradient (AG), spatial frequency (SF), modified spatial frequency (MSF), cross-correlation (CC), mutual information (MI), and fusion symmetry (FS).

Enhanced multimodal medical image fusion based on Pythagorean fuzzy set: an innovative approach.

Medical image fusion is the process of combining a multi-modality image into a single output image for superior information and a better visual appearance without any vagueness or uncertainties. It is suitable for better diagnosis. Pythagorean fuzzy set (PFS)-based medical image fusion was proposed in this manuscript. In the first phase, a two-scale gaussian filter was used to decompose the source images into base and detail layers. In the second phase, a spatial frequency (SF)-based fusion rule was employed for detail layers to preserve the more edge-oriented details. However, the base layer images were converted into pythagorean fuzzy images (PFIs) using the optimum value obtained by pythagorean fuzzy entropy (PFE). The blackness and whiteness count fusion rule were performed for image blocks decomposed from two PFIs in the third phase. Finally, the enhanced fused image was obtained by reconstructions of fused PFI blocks, which performed the defuzzification process. The proposed method was evaluated on different datasets for disease diagnosis and achieved better mean (M), standard deviation (SD), average gradient (AG), SF, modified spatial frequency (MSF), mutual information (MI), and fusion symmetry (FS) values than compared to state-of-art methods. This advancement is important in the field of healthcare and medical imaging, including enhanced diagnostics and treatment planning.

Recent Advancements in Multimodal Medical Image Fusion Techniques for Better Diagnosis: An Overview.

Medical imaging plays a vital role in medical diagnosis and clinical treatment. The biggest challenge in the medical field is the correct identification of disease and better treatment. Multi-modal Medical Image Fusion (MMIF) is the process of merging multiple medical images from different modalities into a single fused image. The main objective of the medical image fusion is to obtain a large amount of appropriate information (i.e., features) to improve the quality and make it more informative for increasing clinical therapy for better diagnosis and clear assessment of medical-related problems. The MMIF is generally considered with MRI (Magnetic Resonance Imaging), CT (Computed Tomography), PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography), MRA (Magnetic Resonance Angiography), T1-weighted MR, T2-weighted MR, X-ray, and ultrasound imaging (Vibro-Acoustography). This review article presents a comprehensive survey of existing medical image fusion methods and has been characterized into six parts: (1) Multi-modality medical images, (2) Literature review process, (3) Image fusion rules, (4) Quality evaluation metrics for assessment of fused image, (5) Experimental results on registered datasets and (6) Conclusion. In addition, this review article provides scientific challenges faced in MMIF and future directions for better diagnosis. It is expected that this review will be useful in establishing a concrete foundation for developing more valuable fusion methods for medical diagnosis.