A deep learning-based approach for detection of lung cancer using self adaptive sea lion optimization algorithm (SA-SLnO)

Pradhan, Kanchan; Chawla, Priyanka; Rawat, Sanyog

doi:10.1007/s12652-022-04118-y

Cited by 13 publications

(2 citation statements)

References 24 publications

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“…To improve accuracy in prediction of prostate cancer, the study employs LSTM recurrent neural network (RNN) and creates Time Series Radiomics (TSR) predictive model [7]. Ensemble classifier approach fine-tunes the critical parameters in each layers of Network [8].Hybrid reduction technique is used to optimizes the process for breast cancer diagnosis [9].Fuzzy C-Means Clustering is applied to optimizes the parameters in RNN through CSO for lung cancer detection [10].A non-invasive breast cancer classification system for Metastatic Breast Cancer (MBC) diagnosis was proposed through ML models using blood profile data of MBC patients for survival prediction [11].Deep Optimal Neurocomputing Technique through Multivariate Analysis is used to predict the cancer which reduces the computation time [12].A innovative technique, termed as self-adaptive sea lion optimization algorithm is used to optimize the weights using cutting-edge meta-heuristic algorithm [13].Adaboost and ensemble machine learning technique predicts early lung cancer. Adaboost proved less sensitive to training set and less prone to overfitting, valuable tool for early lung cancer diagnosis in clinical practice [14].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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Gene data set collected from a diverse population gene expression profiles, genetic variations, and clinical attributes for earlier detection cancer. Time Series Forecasting (TSF) techniques are applied and exploits temporal dependencies within the gene data, enables the prediction of breast cancer and progression. The proposed methodology such as Particle Swarm Optimization-Long Short Term Memory (PSO & LSTM) and Cat Swarm Optimization -Long Short Term Memory (CSO & LSTM) combines with gene data augmentation and analyse the temporal patterns breast cancer genes. Receiver Operating Characteristic (ROC) curve is used for evaluation the proposed models predictive performance. The proposed methods are validated in traditional dataset and collected gene data sets, from National Center for Biotechnology Information (NCBI). The results are compared with existing classification model and evaluated the effectiveness of the TSF methods such as of CSO-LSTM and PSO-LSTM in prediction of breast cancer diseases. The proposed methods contribute to early detection by leveraging time series forecasting techniques. The proposed model improves the accuracy of and reliability of breast cancer prediction, which enables health professional with more information and potentially enhances the patient outcomes

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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“…Due to the increased complexity and low convergence, the proposed work intends to utilize a novel and intelligent optimization algorithm, named as, LBO for feature selection. This technique is developed based on the standard Lion optimization [26] and butterfly optimization [27] algorithms [33]. Typically, butterfly optimization algorithm's foraging and mating behaviors are similar to those of butterflies.…”

Section: Lion-butterfly Optimization (Lbo) Based Feature Selectionmentioning

confidence: 99%

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

Rani S,

et al. 2023

ijcsm

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Lung cancer is one of the most serious and prevalent cancers in the globe. Early detection of lung cancer can increase a patient's chances of life. Computed Tomography (CT) scan images are difficult for clinicians to utilize in order to determine the stages of cancer. Computer-aided systems can assist researchers in more precisely predicting the stages of lung cancer in recent times. This study demonstrates the use of technology that is made possible by machine learning and image processing to accurately classify and predict the lung cancer from CT images. The existing tumor detection frameworks have the major difficulties in terms of high complexity, overfitting and error prediction. Therefore, the proposed work aims to formulate a simple and accurate automated system for the prediction and classification of lung cancer from CT images. Before classifying the input lung scan image, an adaptive median filtering approach is used to improve its contrast and quality. From the segmented lung parts, the histogram and texture features are derived. The most relevant characteristics are chosen using the Lion-Butterfly Optimization (LBO) method for training and testing operations. Eventually, the input CT picture is correctly predicted as either healthy or disease-affected using the Stacking Ensemble Learning Classification (SELC) algorithm. In this study, a thorough performance evaluation is conducted utilizing several measures in order to analyze the outcomes

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A Short Survey Work for Lung Cancer Diagnosis Model: Algorithms Utilized, Challenging Issues, and Future Research Trends

Shaikh,

Shah

2024

Lecture Notes in Networks and Systems

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A deep learning-based approach for detection of lung cancer using self adaptive sea lion optimization algorithm (SA-SLnO)

Cited by 13 publications

References 24 publications

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

An Automated Lion-Butterfly Optimization (LBO) based Stacking Ensemble Learning Classification (SELC) Model for Lung Cancer Detection

A Short Survey Work for Lung Cancer Diagnosis Model: Algorithms Utilized, Challenging Issues, and Future Research Trends

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