Artificial Intelligence in Cancers—2nd Edition

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 (30 September 2024) | Viewed by 3615

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1. Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA 95661, USA
2. Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
Interests: AI (artificial intelligence); medical imaging (ultrasound, MRI, CT); computer-aided diagnosis; machine learning; deep learning; hybrid deep learning; cardiovascular/stroke risk
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Department of Computer Science & Engineering, International Institute of Information Technology, Bhubaneswar 751003, India
Interests: AI techniques in radiomics and radiogenomics (R-n-R) cancer studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cancer is the most common cause of death in developed countries such as the United States, Japan, and the United Kingdom, and it has been shown that the number of patients has a further upsurge in aged people. As per the World Health Organization (WHO), cancer is a leading cause of death worldwide, reporting nearly 10 million deaths in 2020, or almost one in six. The most common cancers include lung, breast, colon, rectum, prostate, and brain. Over the past decade, artificial intelligence (AI) has contributed significantly to resolving various healthcare problems, specifically relating to cancer. Integrating AI and its components such as machine and deep learning in oncology care could lead to progress in prognosis, diagnosis, accuracy, and clinical decision making, leading to better health outcomes. AI-supported clinical care has the potential to play an essential role in addressing health discrepancies, especially in low-resource settings.

This Special Issue invites authors to present their findings, reviews, and challenging experiences of artificial intelligence in different types of human cancers, such as brain, bone, breast, liver, lung, head and neck, gastric, colorectal, and colon.

Dr. Jasjit S. Suri
Dr. Sanjay Saxena
Guest Editors

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Keywords

  • artificial intelligence
  • human cancers
  • prognosis
  • diagnosis
  • accuracy
  • clinical decision making

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Related Special Issue

Published Papers (4 papers)

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Research

27 pages, 6293 KiB  
Article
Lightweight Advanced Deep Neural Network (DNN) Model for Early-Stage Lung Cancer Detection
by Isha Bhatia, Aarti, Syed Immamul Ansarullah, Farhan Amin and Amerah Alabrah
Diagnostics 2024, 14(21), 2356; https://doi.org/10.3390/diagnostics14212356 - 22 Oct 2024
Viewed by 442
Abstract
Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these [...] Read more.
Background: Lung cancer, also known as lung carcinoma, has a high mortality rate; however, an early prediction helps to reduce the risk. In the current literature, various approaches have been developed for the prediction of lung carcinoma (at an early stage), but these still have various issues, such as low accuracy, high noise, low contrast, poor recognition rates, and a high false-positive rate, etc. Thus, in this research effort, we have proposed an advanced algorithm and combined two different types of deep neural networks to make it easier to spot lung melanoma in the early phases. Methods: We have used WDSI (weakly supervised dense instance-level lung segmentation) for laborious pixel-level annotations. In addition, we suggested an SS-CL (deep continuous learning-based deep neural network) that can be applied to the labeled and unlabeled data to improve efficiency. This work intends to evaluate potential lightweight, low-memory deep neural net (DNN) designs for image processing. Results: Our experimental results show that, by combining WDSI and LSO segmentation, we can achieve super-sensitive, specific, and accurate early detection of lung cancer. For experiments, we used the lung nodule (LUNA16) dataset, which consists of the patients’ 3D CT scan images. We confirmed that our proposed model is lightweight because it uses less memory. We have compared them with state-of-the-art models named PSNR and SSIM. The efficiency is 32.8% and 0.97, respectively. The proposed lightweight deep neural network (DNN) model archives a high accuracy of 98.2% and also removes noise more effectively. Conclusions: Our proposed approach has a lot of potential to help medical image analysis to help improve the accuracy of test results, and it may also prove helpful in saving patients’ lives. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancers—2nd Edition)
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21 pages, 2680 KiB  
Article
Multi-View Soft Attention-Based Model for the Classification of Lung Cancer-Associated Disabilities
by Jannatul Ferdous Esha, Tahmidul Islam, Md. Appel Mahmud Pranto, Abrar Siam Borno, Nuruzzaman Faruqui, Mohammad Abu Yousuf, AKM Azad, Asmaa Soliman Al-Moisheer, Naif Alotaibi, Salem A. Alyami and Mohammad Ali Moni
Diagnostics 2024, 14(20), 2282; https://doi.org/10.3390/diagnostics14202282 - 14 Oct 2024
Viewed by 954
Abstract
Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, [...] Read more.
Background: The detection of lung nodules at their early stages may significantly enhance the survival rate and prevent progression to severe disability caused by advanced lung cancer, but it often requires manual and laborious efforts for radiologists, with limited success. To alleviate it, we propose a Multi-View Soft Attention-Based Convolutional Neural Network (MVSA-CNN) model for multi-class lung nodular classifications in three stages (benign, primary, and metastatic). Methods: Initially, patches from each nodule are extracted into three different views, each fed to our model to classify the malignancy. A dataset, namely the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), is used for training and testing. The 10-fold cross-validation approach was used on the database to assess the model’s performance. Results: The experimental results suggest that MVSA-CNN outperforms other competing methods with 97.10% accuracy, 96.31% sensitivity, and 97.45% specificity. Conclusions: We hope the highly predictive performance of MVSA-CNN in lung nodule classification from lung Computed Tomography (CT) scans may facilitate more reliable diagnosis, thereby improving outcomes for individuals with disabilities who may experience disparities in healthcare access and quality. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancers—2nd Edition)
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15 pages, 636 KiB  
Article
The Use of Artificial Intelligence in Predicting Chemotherapy-Induced Toxicities in Metastatic Colorectal Cancer: A Data-Driven Approach for Personalized Oncology
by Eliza-Maria Froicu, Oriana-Maria Oniciuc, Vlad-Adrian Afrăsânie, Mihai-Vasile Marinca, Silvia Riondino, Elena Adriana Dumitrescu, Teodora Alexa-Stratulat, Iulian Radu, Lucian Miron, Gema Bacoanu, Vladimir Poroch and Bogdan Gafton
Diagnostics 2024, 14(18), 2074; https://doi.org/10.3390/diagnostics14182074 - 19 Sep 2024
Viewed by 878
Abstract
Background: Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to [...] Read more.
Background: Machine learning models learn about general behavior from data by finding the relationships between features. Our purpose was to develop a predictive model to identify and predict which subset of colorectal cancer patients are more likely to experience chemotherapy-induced toxicity and to determine the specific attributes that influence the presence of treatment-related side effects. Methods: The predictor was general toxicity, and for the construction of our data training, we selected 95 characteristics that represent the health state of 74 patients prior to their first round of chemotherapy. After the data were processed, Random Forest models were trained to offer an optimal balance between accuracy and interpretability. Results: We constructed a machine learning predictor with an emphasis on assessing the importance of numerical and categorical variables in relation to toxicity. Conclusions: The incorporation of artificial intelligence in personalizing colorectal cancer management by anticipating and overseeing toxicities more effectively illustrates a pivotal shift towards more personalized and precise medical care. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancers—2nd Edition)
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24 pages, 3702 KiB  
Article
An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning
by Isha Bhatia, Aarti, Syed Immamul Ansarullah, Farhan Amin and Amerah Alabrah
Diagnostics 2024, 14(13), 1378; https://doi.org/10.3390/diagnostics14131378 - 28 Jun 2024
Viewed by 788
Abstract
Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an [...] Read more.
Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment. Full article
(This article belongs to the Special Issue Artificial Intelligence in Cancers—2nd Edition)
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