Lung Cancer Detection using Ensemble of Machine Learning Models

Jehangir, Basra; Nayak, Soumya Ranjan; Shandilya, Sourav

doi:10.1109/confluence52989.2022.9734212

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…The benign category was identified with a precision of 1.00 and a recall score of 0.943, yielding an F1-score of 0.971. The SCC category was recognized with a precision of 0.774 and a recall of 0.998, obtaining an F1-score of 0.872 [28].…”

Section: Introductionmentioning

confidence: 99%

A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

Faria

Campelos²,

Carvalho

2023

Applied Sciences

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Lung cancer is a leading cause of cancer-related deaths worldwide, and its diagnosis must be carried out as soon as possible to increase the survival rate. The development of computer-aided diagnosis systems can improve the accuracy of lung cancer diagnosis while reducing the workload of pathologists. The purpose of this study was to develop a learning algorithm (CancerDetecNN) to evaluate the presence or absence of tumor tissue in lung whole-slide images (WSIs) while reducing the computational cost. Three existing deep neural network models, including different versions of the CancerDetecNN algorithm, were trained and tested on datasets of tumor and non-tumor tiles extracted from lung WSIs. The fifth version of CancerDetecNN (CancerDetecNN Version 5) outperformed all existing convolutional neural network (CNN) models in the provided dataset, achieving higher precision (0.972), an area under the curve (AUC) of 0.923, and an F1-score of 0.897, while requiring 1 h and 51 min less for training than the best compared CNN model (ResNet-50). The results for CancerDetecNN Version 5 surpass the results of some architectures used in the literature, but the relatively small size and limited diversity of the dataset used in this study must be considered. This paper demonstrates the potential of CancerDetecNN Version 5 for improving lung cancer diagnosis since it is a dedicated model for lung cancer that leverages domain-specific knowledge and optimized architecture to capture unique characteristics and patterns in lung WSIs, potentially outperforming generic models in this domain and reducing the computational cost.

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Section: Introductionmentioning

confidence: 99%

A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

Faria

Campelos²,

Carvalho

2023

Applied Sciences

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Optimized AI Bed for Maim and Bedridden Elder Person Based on Mobile Application

Jones Daniel,

Sundar Rajan

2024

Studies in Autonomic, Data-Driven and Industrial Computing

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ML-based early detection of lung cancer: an integrated and in-depth analytical framework

Sinjanka,

Kaur,

Musa

et al. 2024

Discov Artif Intell

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The human lungs, crucial for supplying oxygen, are vulnerable to diseases such as lung cancer, a leading cause of mortality. Timely prediction of lung cancer is essential to enable early intervention by healthcare professionals, enhancing patient outcomes and saving lives. This study introduces a comprehensive Machine Learning (ML) model designed to predict lung cancer at an early stage, utilizing a dataset sourced from Kaggle. Built on the Random Forest algorithm, the model assesses a diverse set of characteristics and variables, including gender, age, and exposure to various environments and lifestyles. It accurately identifies individuals at a higher risk of developing early-stage lung cancer, facilitating prompt intervention and personalized treatment strategies. Key evaluation metrics demonstrating the model's effectiveness include precision, F1 score, recall, and accuracy. The findings indicate a model accuracy of approximately 97.9%, underscoring its potential as a valuable tool for enhancing the early detection of lung cancer.

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Lung Cancer Detection using Ensemble of Machine Learning Models

Cited by 6 publications

References 19 publications

A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection

Optimized AI Bed for Maim and Bedridden Elder Person Based on Mobile Application

ML-based early detection of lung cancer: an integrated and in-depth analytical framework

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