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A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 18912

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Dr. Vivek Kumar Singh

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Tissue Hybridisation & Digital Pathology, Precision Medicine Centre of Excellence, Queen’s University Belfast, Northern Ireland, 97 Lisburn Rd, Belfast BT9 7AE, UK
Interests: breast cancer; breast density; deep learning; mammograms; generative adversarial networks; convolutional neural network; COVID-19; ct slices; image segmentation
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Special Issue Information

Dear Colleagues,

Breast cancer is the most frequently diagnosed cause of death from cancer in women worldwide. According to the World Health Organization (WHO), in 2020, around 2.3 million women were diagnosed with breast cancer, and 685,000 have died. Early identification plays a crucial role in reducing the mortality rate. To build an automated solution, the recent development of machine learning (ML) and deep learning (DL) techniques allows an enhancement of the accuracy of cancer screening.

According to the focus of this Special Issue of Diagnostics, “Machine Learning in Breast Disease Diagnosis”, we invite research manuscripts on topics of translational research that address breast cancer predicting prognosis by use of artificial intelligence. Furthermore, research on molecular subtype prediction for reducing biopsies is also of interest, as well as on the prognosis that deals with malignant tumor grading, including malignancy stage classification. This Special Issue welcomes translational studies with multiple imaging modalities, such as mammograms, ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histopathological images, as well as clinical data. The main aim is to utilize the ML and DL methods that provide a robust solution for clinical practice.

Dr. Vivek Kumar Singh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

machine learning
deep learning
classification
segmentation
reconstruction
detection

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Published Papers (5 papers)

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Research

12 pages, 1977 KiB

Open AccessArticle

Optimized S-Curve Transformation and Wavelets-Based Fusion for Contrast Elevation of Breast Tomograms and Mammograms

by Vikrant Bhateja, Shabana Urooj, Anushka Dikshit and Ashruti Rai

Diagnostics 2023, 13(3), 410; https://doi.org/10.3390/diagnostics13030410 - 23 Jan 2023

Cited by 2 | Viewed by 1617

Abstract

For the purpose of accuracy in detection and diagnosis, Computer-Aided Diagnosis (CAD) is preferred by radiologists for the analysis of Breast Cancer. However, the presence of noise, artifacts, and poor contrast in breast images during acquisition highlights the need for sophisticated enhancement techniques for the proper visualization of region-of-interest (ROI). In this work, contrast elevation of breast mammographic and tomographic images is performed with an improved S-Curve transform using the Particle Swarm Optimization (PSO) algorithm. The enhanced images are assessed using dedicated quality metrics such as the Enhancement Measure (EME) and Absolute Mean Brightness Error (AMBE) measurement. Although the enhancement techniques help in attaining better images, certain features relevant for diagnosis purposes are removed during the enhancement process, creating contradictions for radiological interpretation. Hence, to ensure the retention of diagnostic features from original breast tomograms and mammograms, a Discrete Wavelet Transform (DWT)-based fusion approach is incorporated, which fuses the original and contrast-enhanced images (with optimized s-curve transformation function) using the maximum fusion rule. The fusion performance is thereafter measured using the Image Quality Index (IQI), Standard Deviation (SD), and Entropy (E) as fusion metrics. Full article

(This article belongs to the Special Issue Machine Learning in Breast Disease Diagnosis)

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17 pages, 4516 KiB

Open AccessArticle

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

by Mohamed A. Hassanien, Vivek Kumar Singh, Domenec Puig and Mohamed Abdel-Nasser

Diagnostics 2022, 12(5), 1053; https://doi.org/10.3390/diagnostics12051053 - 22 Apr 2022

Cited by 34 | Viewed by 4536

Abstract

Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods. Full article

(This article belongs to the Special Issue Machine Learning in Breast Disease Diagnosis)

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19 pages, 6683 KiB

Open AccessArticle

Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

by Yaghoub Pourasad, Esmaeil Zarouri, Mohammad Salemizadeh Parizi and Amin Salih Mohammed

Diagnostics 2021, 11(10), 1870; https://doi.org/10.3390/diagnostics11101870 - 11 Oct 2021

Cited by 22 | Viewed by 4211

Abstract

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area. Full article

(This article belongs to the Special Issue Machine Learning in Breast Disease Diagnosis)

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11 pages, 3287 KiB

Open AccessArticle

Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks

by Elham Yousef Kalafi, Ata Jodeiri, Seyed Kamaledin Setarehdan, Ng Wei Lin, Kartini Rahmat, Nur Aishah Taib, Mogana Darshini Ganggayah and Sarinder Kaur Dhillon

Diagnostics 2021, 11(10), 1859; https://doi.org/10.3390/diagnostics11101859 - 9 Oct 2021

Cited by 27 | Viewed by 3928

Abstract

The reliable classification of benign and malignant lesions in breast ultrasound images can provide an effective and relatively low-cost method for the early diagnosis of breast cancer. The accuracy of the diagnosis is, however, highly dependent on the quality of the ultrasound systems and the experience of the users (radiologists). The use of deep convolutional neural network approaches has provided solutions for the efficient analysis of breast ultrasound images. In this study, we propose a new framework for the classification of breast cancer lesions with an attention module in a modified VGG16 architecture. The adopted attention mechanism enhances the feature discrimination between the background and targeted lesions in ultrasound. We also propose a new ensembled loss function, which is a combination of binary cross-entropy and the logarithm of the hyperbolic cosine loss, to improve the model discrepancy between classified lesions and their labels. This combined loss function optimizes the network more quickly. The proposed model outperformed other modified VGG16 architectures, with an accuracy of 93%, and also, the results are competitive with those of other state-of-the-art frameworks for the classification of breast cancer lesions. Our experimental results show that the choice of loss function is highly important and plays a key role in breast lesion classification tasks. Additionally, by adding an attention block, we could improve the performance of the model. Full article

(This article belongs to the Special Issue Machine Learning in Breast Disease Diagnosis)

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15 pages, 3427 KiB

Open AccessArticle

An Artificial Intelligence-Enabled Pipeline for Medical Domain: Malaysian Breast Cancer Survivorship Cohort as a Case Study

by Mogana Darshini Ganggayah, Sarinder Kaur Dhillon, Tania Islam, Foad Kalhor, Teh Chean Chiang, Elham Yousef Kalafi and Nur Aishah Taib

Diagnostics 2021, 11(8), 1492; https://doi.org/10.3390/diagnostics11081492 - 18 Aug 2021

Cited by 3 | Viewed by 3297

Abstract

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer. Full article

(This article belongs to the Special Issue Machine Learning in Breast Disease Diagnosis)

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