Small‑cell lung carcinoma. Cytological diagnosis

Grygoruk, O. G.; Tsoi, D. A.; Bazulina, L. M.; Vihlyanov, I. V.

doi:10.18027/2224-5057-2022-12-1-36-43

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…And the validation tried out with various techniques such as carcinomas synthesize immune regulatory glucocorticoids [11], With targeted therapy or immunotherapy, the histomorphological transition from non-small cell lung carcinoma to small cell lung cancer occurs [12], Small-cell lung carcinoma. Cytological diagnosis [13], But artificial intelligence has opened new paradigm for prediction and diagnosis of these diseases. Various disease like epileptic seizure detection [14], cardiac condition prediction, lung cancer stages, disease prediction from frequency of eye blinking [15] etc.…”

Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer

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Section: Literature Surveymentioning

confidence: 99%

Supervised learning techniques for detection of Lung Carcinoma

Jalall,

Harsha,

Dutta

et al. 2023

J. Phys.: Conf. Ser.

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Clinical and Radiological Parallels in the Diagnosis of Primary Lung Cancer Using Mathematical Modeling Technologies

Borisenko,

Lazarev

2024

Russian Journal of Oncology

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The problem of lung cancer (LC) is becoming increasingly relevant every year. According to the cancer registry of the Altai Regional Oncology Center, the incidence of lung cancer in 2019 was 114.8 per 100 thousand population for men, 19.3 per 100 thousand population for women; in 2020 – 96.8 and 16.8, respectively. In 2021, the incidence was 108.9 per 100 thousand population. Diagnostic rates in 2022 among patients with respiratory cancer were disappointing, since at the time of diagnosis 42.2% had stage IV of the disease, 27.9% had stage III, 16.3% had stage I and 12.9% had stage II. and in 0.7% of cases the stage was not established. When analyzing the distribution of LC patients of different age groups depending on the tumor histotype, it was found that the majority are adenocarcinoma and squamous cell LC – 85%. Our study used MSCT data from 485 patients with LC aged from 45 to 80 years. Digital analysis of the scans was carried out using the “Radiologist+” program (Russia, Barnaul), which allows direct sampling of average pixel densities in tabular form in selected areas of interest from files in DICOM format for subsequent analysis and statistical processing. The resulting densitometric indicators were fed to the inputs of the artificial neural network. The effectiveness of differential diagnosis of histological forms, taking into account the presence or absence of tobacco smoking in patients: sensitivity 85.8%, specificity 85.0%, accuracy 85.4%.

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Small‑cell lung carcinoma. Cytological diagnosis

Cited by 2 publications

References 24 publications

Supervised learning techniques for detection of Lung Carcinoma

Supervised learning techniques for detection of Lung Carcinoma

Clinical and Radiological Parallels in the Diagnosis of Primary Lung Cancer Using Mathematical Modeling Technologies

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