Artificial Neural Networks in Lung Cancer Research: A Narrative Review

Prisciandaro, Elena; Sedda, Giulia; Cara, Andrea; Diotti, Cristina; Spaggiari, Lorenzo

doi:10.3390/jcm12030880

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…The index evaluations included machine learning AI algorithms for analyzing medical images for lung cancer detection. The ML architectures considered for inclusion in the study comprised neural networks and CADs that are built on machine learning models [ 23 , 24 ]. The ML algorithms used radiological parameters to determine the presence of lung cancer and classify the nodules.…”

Section: Methodsmentioning

confidence: 99%

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Pacurari,

Bhattarai,

Muhammad

et al. 2023

Diagnostics

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The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures currently in use, and evaluate the clinical relevance of these diagnostic imaging methods. A systematic search of PubMed, Web of Science, Cochrane, and Scopus databases was conducted in February 2023, encompassing the literature published up until December 2022. The review included nine studies, comprising five case-control studies, three retrospective cohort studies, and one prospective cohort study. Various ML architectures were analyzed, including artificial neural network (ANN), entropy degradation method (EDM), probabilistic neural network (PNN), support vector machine (SVM), partially observable Markov decision process (POMDP), and random forest neural network (RFNN). The ML architectures demonstrated promising results in detecting and classifying lung cancer across different lesion types. The sensitivity of the ML algorithms ranged from 0.81 to 0.99, while the specificity varied from 0.46 to 1.00. The accuracy of the ML algorithms ranged from 77.8% to 100%. The AI architectures were successful in differentiating between malignant and benign lesions and detecting small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). This systematic review highlights the potential of ML AI architectures in the detection and classification of lung cancer, with varying levels of diagnostic accuracy. Further studies are needed to optimize and validate these AI algorithms, as well as to determine their clinical relevance and applicability in routine practice.

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Section: Methodsmentioning

confidence: 99%

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Pacurari,

Bhattarai,

Muhammad

et al. 2023

Diagnostics

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show abstract

“…SNN merupakan jaringan saraf tiruan yang memiliki kemiripan dengan sistem kinerja otak, yaitu dengan memadukan kemampuan neuron (sel atau saraf) pada otak manusia untuk berkomunikasi menerima respon melalui sinyal listrik (nervous system) sebagai data. Perubahan data dalam ruang dan waktu direpresentasikan sebagai peristiwa biner (spikes) sehingga mampu ditunjukkan melalui nilai kuantitatif untuk mengetahui keakuratan AI (Prisciandaro et al, 2023). Algoritma SNN mengkodekan informasi eksternal menjadi pulsa listrik.…”

Section: Pendahuluanunclassified

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

Ningtias,

Rofi’i,

Pramudita

2024

JITE

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The large number of cases of lung disease means that doctors have difficulty in making initial diagnoses, making them prone to misdiagnoses. One type of lung disease that is included in the vulnerable category is pneumonia. Early detection of the condition of the lungs affected by bacterial pneumonia can be carried out by screening using the X-Ray examination modality, namely Digital Radiography (DR). However, in practice, the diagnosis process on Citra DR takes a long time because it requires competent medical personnel (specialists). A system is needed that can help medical personnel to speed up the process of diagnosing lung disease and get accurate results so that misdiagnosis does not occur. The aim of this research is to utilize the Spiking Neural Network (SNN) method for classifying lung disease from DR images. The system was created using MATLAB with the initial step of creating a read data program, namely reading DR image secondary data in .jpg format taken from Kaggle.com. This research uses DR image data totaling 200 images. Next, standardize the size to 50 x 50 pixels. Then segmenting the image divides the gray level histogram into two different parts of the image automatically without requiring user assistance to enter threshold values for normal and pneumonia images. Then convert the image to 1 dimension and create a manual program for the training data using 50 normal images and 50 pneumonia images. Lastly, create a program to test the data using 100 normal images and 100 pneumonia images. Based on the results of data testing, a confusion matrix was obtained from 200 images with sensitivity of 87%, specificity of 69%, precision of 73.7288%, recall of 69%, and accuracy of 78%

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“…They play a fundamental role in clinical decision-making, although their success depends on good integration with clinical protocols. ANN have shown excellent aptitude in learning the relationships between the input/output mapping from a given dataset, without any prior information or assumptions about the statistical distribution of the data [2]. Artificial Neural Networks (ANNs) have proven to be effective for modeling decision-making problems in medicine, including diagnostics, prediction, resource allocation, and cost reduction problems.…”

Section: аннотацияmentioning

confidence: 99%

Investigation of Data on Morphological Classification of Clinical and Hematological Syndromes Based on Machine Learning Algorithms

Uvaliyeva,

Belginova,

Borozenets

2023

BEKTU

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Currently, the greatest attention is paid to the problem of the development and application of medical information systems, their integration in the direction of building a single information space. There is a need to develop decision support systems that are direct «assistants» of doctors in the medical and diagnostic process and should find their place in integrated systems. The urgency of the problem lies in the need to develop intelligent medical systems based on effective methods, algorithms and models to support medical decision- making in conditions of incompleteness and uncertainty of the initial data of the medical and technological process, allowing to ensure high adequacy and validity of decisions made in conditions of limited time resources. To solve this problem, this article proposes a study of data on the morphological classification of clinical and hematological syndromes based on machine learning algorithms. The use of machine learning algorithms for indicators of clinical and hematological sy ndromes will increase the effectiveness of differential diagnosis and apply it to the development of algorithmic and software for an intelligent system to support clinical decision-making.

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Artificial Neural Networks in Lung Cancer Research: A Narrative Review

Cited by 16 publications

References 23 publications

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Diagnostic Accuracy of Machine Learning AI Architectures in Detection and Classification of Lung Cancer: A Systematic Review

Utilization of Spiking Neural Network (SNN) in X-Ray Image for Lung Disease Detection

Investigation of Data on Morphological Classification of Clinical and Hematological Syndromes Based on Machine Learning Algorithms

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