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Biomedicines
Special Issues
Machine Learning in Oncology: Current Status and Future Perspectives
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Machine Learning in Oncology: Current Status and Future Perspectives

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Molecular and Translational Medicine".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1666

Special Issue Editors

Dr. Nelson Yee

E-Mail Website
Guest Editor
Penn State Cancer Institute, Penn State Health Milton S. Hershey Medical Center, 500 University Drive, Hershey, PA 17033-0850, USA
Interests: cancer biomarkers, cancer therapy, clinical investigation, machine learning, translational research
Special Issues, Collections and Topics in MDPI journals
Dr. Fenglong Ma

E-Mail Website
Guest Editor
College of Information Sciences and Technology, Institute for Computational and Data Sciences, Pennsylvania State University, State College, PA 16802, USA
Interests: machine learning; natural language progessing; healthcare data mining; healthcare; informatics

Special Issue Information

Dear Colleagues,

Despite steady progress in basic and clinical research, cancer is still among the most challenging of human diseases. The accurate prediction of risk, early detection, diagnosis, and effective and safe treatment of various malignancies remain as major unmet needs in oncology. Artificial intelligence, a field of applied computer science, has accelerated the evolution of nearly every aspect of human lives, including healthcare. The application of data-driven machine learning (ML) and deep learning (DL) in translational research has shown great promise for advancing cancer diagnosis and treatment outcomes. ML-based algorithms for the analysis of radiological and histological images have been shown to enable detection and improve diagnostic accuracy in cancer. DL-based models using multi-omics and molecular datasets have provided opportunities to facilitate drug discovery and treatment. Validation and test datasets from cancer patients will enable the assessment of ML-created model effectiveness in oncology. The aim of this Special Issue is to collect articles that focus on machine learning for data analytics, with the goal of advancing the frontiers in clinical oncology and cancer research.

Dr. Nelson Yee
Dr. Fenglong Ma
Guest Editors

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

  • machine learning
  • cancer
  • risk
  • early detection
  • diagnosis treatment
  • drug discovery
  • healthcare data mining
  • natural language processing
  • healthcare informatics

Published Papers (1 paper)

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Research

18 pages, 19618 KiB  
Article
Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction
by Jothi Prabha Appadurai, Suganeshwari G, Balasubramanian Prabhu Kavin, Kavitha C and Wen-Cheng Lai
Biomedicines 2023, 11(3), 679; https://doi.org/10.3390/biomedicines11030679 - 23 Feb 2023
Viewed by 1315
Abstract
In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora [...] Read more.
In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction. Full article
(This article belongs to the Special Issue Machine Learning in Oncology: Current Status and Future Perspectives)
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