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Biomedicines
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Artificial Intelligence Applications in Cancer and Other Diseases
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Artificial Intelligence Applications in Cancer and Other Diseases

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Biomedical Engineering and Materials".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 4805

Special Issue Editor

Prof. Dr. Raji Sundararajan

E-Mail Website
Guest Editor
School of Engineering Technology, Purdue University, Knoy Hall of Technology, West Lafayette, IN 47907, USA
Interests: artificial intelligence; machine learning; neural networks; deep learning; obesity; diabetes; cancer; other diseases; pathology; drug discovery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and its subsets, machine learning, neural networks, deep learning, etc., have the potential to revolutionize the medical field. AI is not only useful for analyzing medical images, such as ECG, EEG, etc., but is also useful for labelled and unlabeled data. Various machine learning algorithms, such as naïve basis, support vector machines (SVMs), etc., are useful in predicting breast cancer occurrence, pattern, and early detection. AI can be used for both communicable and non-communicable diseases. The supervised learning, unsupervised learning, and semi-supervised learning models of machine learning have advanced algorithms to work on the type of data available in addition to the images most commonly used in this kind of research. With the possibility of one out of two men and one out of three women suffering from cancer in the US, as well as the global increase in obesity, diabetes, cancer, and other diseases, the need for additional tools, besides conventional ones, such as AI in the early detection and prediction of cancer and other diseases, as well as its applications in pathology, drug discovery, etc., cannot be overstated. Towards this end, this Special Issue invites original research articles as well as detailed review articles and short communications on the applications of AI in cancer and other diseases.

Prof. Dr. Raji Sundararajan
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. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • neural networks
  • deep learning
  • obesity
  • diabetes
  • cancer
  • other diseases
  • pathology
  • drug discovery

Published Papers (4 papers)

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Research

18 pages, 7405 KiB  
Article
Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images
by Eunmok Yang, K. Shankar, Sachin Kumar, Changho Seo and Inkyu Moon
Biomedicines 2023, 11(12), 3200; https://doi.org/10.3390/biomedicines11123200 - 01 Dec 2023
Viewed by 936
Abstract
The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic [...] Read more.
The enlargement of the prostate gland in the reproductive system of males is considered a form of prostate cancer (PrC). The survival rate is considerably improved with earlier diagnosis of cancer; thus, timely intervention should be administered. In this study, a new automatic approach combining several deep learning (DL) techniques was introduced to detect PrC from MRI and ultrasound (US) images. Furthermore, the presented method describes why a certain decision was made given the input MRI or US images. Many pretrained custom-developed layers were added to the pretrained model and employed in the dataset. The study presents an Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique on MRIs. The main goal of the EOADL-PCDC method lies in the detection and classification of PrC. To achieve this, the EOADL-PCDC technique applies image preprocessing to improve the image quality. In addition, the EOADL-PCDC technique follows the CapsNet (capsule network) model for the feature extraction model. The EOA is based on hyperparameter tuning used to increase the efficiency of CapsNet. The EOADL-PCDC algorithm makes use of the stacked bidirectional long short-term memory (SBiLSTM) model for prostate cancer classification. A comprehensive set of simulations of the EOADL-PCDC algorithm was tested on the benchmark MRI dataset. The experimental outcome revealed the superior performance of the EOADL-PCDC approach over existing methods in terms of different metrics. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases)
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11 pages, 3266 KiB  
Article
A Radiotherapy Dose Map-Guided Deep Learning Method for Predicting Pathological Complete Response in Esophageal Cancer Patients after Neoadjuvant Chemoradiotherapy Followed by Surgery
by Wing-Keen Yap, Ing-Tsung Hsiao, Wing-Lake Yap, Tsung-You Tsai, Yi-An Lu, Chan-Keng Yang, Meng-Ting Peng, En-Lin Su and Shih-Chun Cheng
Biomedicines 2023, 11(11), 3072; https://doi.org/10.3390/biomedicines11113072 - 16 Nov 2023
Cited by 2 | Viewed by 1134
Abstract
Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy [...] Read more.
Esophageal cancer is a deadly disease, and neoadjuvant chemoradiotherapy can improve patient survival, particularly for patients achieving a pathological complete response (ypCR). However, existing imaging methods struggle to accurately predict ypCR. This study explores computer-aided detection methods, considering both imaging data and radiotherapy dose variations to enhance prediction accuracy. It involved patients with node-positive esophageal squamous cell carcinoma undergoing neoadjuvant chemoradiotherapy and surgery, with data collected from 2014 to 2017, randomly split into five subsets for 5-fold cross-validation. The algorithm DCRNet, an advanced version of OCRNet, integrates RT dose distribution into dose contextual representations (DCR), combining dose and pixel representation with ten soft regions. Among the 80 enrolled patients (mean age 55.68 years, primarily male, with stage III disease and middle-part lesions), the ypCR rate was 28.75%, showing no significant demographic or disease differences between the ypCR and non-ypCR groups. Among the three summarization methods, the maximum value across the CTV method produced the best results with an AUC of 0.928. The HRNetV2p model with DCR performed the best among the four backbone models tested, with an AUC of 0.928 (95% CI, 0.884–0.972) based on 5-fold cross-validation, showing significant improvement compared to other models. This underscores DCR-equipped models’ superior AUC outcomes. The study highlights the potential of dose-guided deep learning in ypCR prediction, necessitating larger, multicenter studies to validate the results. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases)
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21 pages, 6153 KiB  
Article
Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules
by Yu-Cheng Tung, Ja-Hwung Su, Yi-Wen Liao, Yeong-Chyi Lee, Bo-An Chen, Hong-Ming Huang, Jia-Jhan Jhang, Hsin-Yi Hsieh, Yu-Shun Tong, Yu-Fan Cheng, Chien-Hao Lai and Wan-Ching Chang
Biomedicines 2023, 11(11), 2938; https://doi.org/10.3390/biomedicines11112938 - 30 Oct 2023
Viewed by 944
Abstract
Over the past few decades, recognition of early lung cancers was researched for effective treatments. In early lung cancers, the invasiveness is an important factor for expected survival rates. Hence, how to effectively identify the invasiveness by computed tomography (CT) images became a [...] Read more.
Over the past few decades, recognition of early lung cancers was researched for effective treatments. In early lung cancers, the invasiveness is an important factor for expected survival rates. Hence, how to effectively identify the invasiveness by computed tomography (CT) images became a hot topic in the field of biomedical science. Although a number of previous works were shown to be effective on this topic, there remain some problems unsettled still. First, it needs a large amount of marked data for a better prediction, but the manual cost is high. Second, the accuracy is always limited in imbalance data. To alleviate these problems, in this paper, we propose an effective CT invasiveness recognizer by semi-automated segmentation. In terms of semi-automated segmentation, it is easy for doctors to mark the nodules. Just based on one clicked pixel, a nodule object in a CT image can be marked by fusing two proposed segmentation methods, including thresholding-based morphology and deep learning-based mask region-based convolutional neural network (Mask-RCNN). For thresholding-based morphology, an initial segmentation is derived by adaptive pixel connections. Then, a mathematical morphology is performed to achieve a better segmentation. For deep learning-based mask-RCNN, the anchor is fixed by the clicked pixel to reduce the computational complexity. To incorporate advantages of both, the segmentation is switched between these two sub-methods. After segmenting the nodules, a boosting ensemble classification model with feature selection is executed to identify the invasiveness by equalized down-sampling. The extensive experimental results on a real dataset reveal that the proposed segmentation method performs better than the traditional segmentation ones, which can reach an average dice improvement of 392.3%. Additionally, the proposed ensemble classification model infers better performances than the compared method, which can reach an area under curve (AUC) improvement of 5.3% and a specificity improvement of 14.3%. Moreover, in comparison with the models with imbalance data, the improvements of AUC and specificity can reach 10.4% and 33.3%, respectively. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases)
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15 pages, 3792 KiB  
Article
ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI
by Nevsun Pihtili Tas, Oguz Kaya, Gulay Macin, Burak Tasci, Sengul Dogan and Turker Tuncer
Biomedicines 2023, 11(9), 2441; https://doi.org/10.3390/biomedicines11092441 - 01 Sep 2023
Cited by 7 | Viewed by 1255
Abstract
Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose [...] Read more.
Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Cancer and Other Diseases)
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