7 AI-driven insights: a machine learning approach to lung cancer diagnosis
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Saboor Uddin Ahmed
Abstract
Lung discomfort emerges as a prevalent early symptom in cancer treatment, posing significant diagnostic challenges due to delays in radiologist assessments. To address this, we propose an advanced computational framework designed to assist radiologists in the precise detection of lung cancer. This study introduces a multi-tiered prognostic model leveraging cutting-edge machine learning (ML) techniques. The segmentation process employs a threshold-based and marker-controlled watershed algorithm, coupled with a dual-classifier system, to enhance data refinement and accuracy. Detecting lung cancer demands exceptional sensitivity, achieved through training on a curated dataset using sophisticated algorithms including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), and Random Forest (RF). Notably, the Random Forest algorithm delivers a superior performance metric, achieving an accuracy of 88.5 %. This innovative approach underscores the transformative potential of integrating advanced ML methodologies with radiological expertise to revolutionize early lung cancer diagnosis and improve patient outcomes.
Abstract
Lung discomfort emerges as a prevalent early symptom in cancer treatment, posing significant diagnostic challenges due to delays in radiologist assessments. To address this, we propose an advanced computational framework designed to assist radiologists in the precise detection of lung cancer. This study introduces a multi-tiered prognostic model leveraging cutting-edge machine learning (ML) techniques. The segmentation process employs a threshold-based and marker-controlled watershed algorithm, coupled with a dual-classifier system, to enhance data refinement and accuracy. Detecting lung cancer demands exceptional sensitivity, achieved through training on a curated dataset using sophisticated algorithms including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression (LR), Naive Bayes (NB), and Random Forest (RF). Notably, the Random Forest algorithm delivers a superior performance metric, achieving an accuracy of 88.5 %. This innovative approach underscores the transformative potential of integrating advanced ML methodologies with radiological expertise to revolutionize early lung cancer diagnosis and improve patient outcomes.
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
Chapters in this book
- Frontmatter I
- Contents V
- List of Contributing Authors VII
- 1 Introduction: fundamentals of drug discovery, telemedicine, artificial intelligence, computer vision, and IoT 1
- 2 Machine learning transformations in drug discovery: a paradigm shift in development strategies 11
- 3 Explainable AI approaches in drug classification from biomarkers of epileptic seizure 27
- 4 Harnessing predictive analytics and machine learning in personalized medicine: patient outcomes and public health strategies 41
- 5 A data-driven framework for future healthcare diagnosis through predictive analytics 59
- 6 Revolutionizing home healthcare: telemedicine, predictive analytics, and AI-driven drug discovery 71
- 7 AI-driven insights: a machine learning approach to lung cancer diagnosis 91
- 8 Efficient gene selection for breast cancer classification using Brownian Motion Search Algorithm and Support Vector Machine 109
- 9 A hybrid feature gene selection approach by integrating variance filter, extremely randomized tree, and Cuckoo Search algorithm for cancer classification 127
- 10 HySleep_Net: a hybrid deep learning model for automatic sleep stage detection from polysomnographic signals 151
- 11 Ambulance booking and tracking website 183
- 12 Entropy based emergency rescue location selection with uncertain travel time 207
- 13 Performance comparison of different deep learning ensemble models for sentiment classification of movie reviews 225
- 14 Elevating standards in homoeopathic medicine: chemometric standardization of medicinal plant for quality assurance 253
- 15 Evaluation of genetic diversity in Rauvolfia species using Random Amplification of Polymorphic DNA (RAPD) technique 259
- Index
