An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

Mokni, Raouia; Gargouri, Norhene; Damak, Alima; Sellami, Dorra; Feki, W.; Mnif, Z.

doi:10.1016/j.bspc.2021.102914

Cited by 31 publications

(12 citation statements)

References 31 publications

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“…Data from each modality are applied individually or combined with others. In 374 articles, im- age modality was present in 247, which were image data individually used in 129 articles [49], [51], [52], [54], [56], [57], [59]- [61], [63], [64] [103], [105], [106], [108], [110], [112]- [115], [117], [120]- [123], [128], [130], [134], [135], [137]- [149], [151], [152], [154], [155], [157], [158], [160], [162], [172]- [174], [176], [178], [191], [194]- [196], [198], [202], [203], [209], [210], [212], [214],…”

Section: B Taskmentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role by tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.

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Section: B Taskmentioning

confidence: 99%

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

2023

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“…In [ 19 ], the authors described the use of a Faster R-CNN object detector with four function extractors: Resnet-50, VGG-16, Inception-V2, and Resnet-101 for automatic lymphocyte detection and counting. In [ 20 ], the authors presented an automatic computer-aided diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD).…”

Section: Literature Reviewmentioning

confidence: 99%

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

Berezsky

Pitsun²,

Melnyk³

et al. 2023

J. Imaging

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The paper explored the problem of automatic diagnosis based on immunohistochemical image analysis. The issue of automated diagnosis is a preliminary and advisory statement for a diagnostician. The authors studied breast cancer histological and immunohistochemical images using the following biomarkers progesterone, estrogen, oncoprotein, and a cell proliferation biomarker. The authors developed a breast cancer diagnosis method based on immunohistochemical image analysis. The proposed method consists of algorithms for image preprocessing, segmentation, and the determination of informative indicators (relative area and intensity of cells) and an algorithm for determining the molecular genetic breast cancer subtype. An adaptive algorithm for image preprocessing was developed to improve the quality of the images. It includes median filtering and image brightness equalization techniques. In addition, the authors developed a software module part of the HIAMS software package based on the Java programming language and the OpenCV computer vision library. Four molecular genetic breast cancer subtypes could be identified using this solution: subtype Luminal A, subtype Luminal B, subtype HER2/neu amplified, and basalt-like subtype. The developed algorithm for the quantitative characteristics of the immunohistochemical images showed sufficient accuracy in determining the cancer subtype “Luminal A”. It was experimentally established that the relative area of the nuclei of cells covered with biomarkers of progesterone, estrogen, and oncoprotein was more than 85%. The given approach allows for automating and accelerating the process of diagnosis. Developed algorithms for calculating the quantitative characteristics of cells on immunohistochemical images can increase the accuracy of diagnosis.

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“…Texture analysis is relevant attribute for mammographic image analysis, it yields very good results ([19] [20] [21] [22] [23] [24]). We consider here the Monogenic Gray Level and Local Difference, it is based on the combination of the GLLD, the LP, and the LS information.…”

Section: The Proposed Systemmentioning

confidence: 99%

An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

Gargouri

Mokni

Damak

et al. 2022

IET Image Processing

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Mammography continues to play a central part in the early breast masses diagnosis and has raised several challenges in breast cancer detection. Nevertheless, it remains still difficult to detect abnormalities in a dense breast and some benign lesions may have similar appearance with masses. This study proposes a novel Computer-Aided Diagnosis (CADx) allowing benign and malignant mass classification. Accordingly, relevant textural-shape descriptors are proposed, which is the aggregation of a new Local Binary based feature, namely the Monogenic Gray Level and Local Difference (MGLLD), where both texture characteristics and breast tissue density information, and the Zernike moments are incorporated. A heuristic algorithm is then devised for optimizing the proposed features with respect to inter-class and intra-class distribution, by a convenient ponderation. Then, a set of classifiers are applied for opting to the best decision making solution. The Deep CNN yields the best accuracy, with an Area Under Curve (AUC) of 0.98 on Curated Breast Imaging Subset of DDSM (CBIS-DDSM). In addition, the authors are based on a subjective approval of the extracted Region Of Interest (ROI) by experts in radiology. The proposed scheme proves its effectiveness especially on some challenging breast cancer cases, corresponding to higher breast tissue density. In medical image processing, these results open new perspectives with respect to the use of the deep learning solutions, where the small sample-number is known to be a limitation (because pathologies cannot be intentionally provoked).

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An automatic Computer-Aided Diagnosis system based on the Multimodal fusion of Breast Cancer (MF-CAD)

Cited by 31 publications

References 31 publications

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

An Approach toward Automatic Specifics Diagnosis of Breast Cancer Based on an Immunohistochemical Image

An automatic breast computer‐aided diagnosis scheme based on a weighted fusion of relevant features and a deep CNN classifier

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