Lung cancer detection using Adaptive Bilateral Filter (ABF) techniques

Asuntha, A; Srinivasan, Andy

doi:10.26452/ijrps.v10i3.1383

Cited by 2 publications

(2 citation statements)

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“…Moreover, accurate diagnosis is essential for developing the best possible treatment plans for each lung cancer patient [15,16]. Therefore, in order to improve prognosis and treatment results, the identification of sensitive and specific biomarkers for early detection has arisen as a crucial necessity in the field [17,18]. Building machine learning models to improve lung cancer detection and prediction accuracy is the main goal of this work.…”

Section: Literature Surveymentioning

confidence: 99%

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

2024

ijacsa

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It is important to note that the cure rates in cases of advanced stages of lung cancer are remarkably low, which stresses out the importance for early detection as means to increase survival chances. A strong area of focus when it comes to increased research in the lung cancer diagnosis is the search for ways through which this disease can be identified at its early stages. The methodology described below is proposed as a means to facilitate early detection of lung cancer. There are two phases in this approach. The study deals with effectiveness of three types of classifiers K-Nearest Neighbors (KNN), Random Forest and Support Vector Machine (SVM) to identify cases related to lung cancer via relevant medical data assessment. In this application, the eval axis performs profiling or measures the accuracy of applying these classifiers and discriminating between cancerous instances versus non-cancerous ones within the dataset. To rate the adequacy of classifiers in distinguishing classes, performance metrics like accuracy, precision, recall and F1-score are used. Furthermore, the research compares KNN, Random Forest and SVM, explaining their specific advantages as well as disadvantages logically referring to how they can or cannot be applied while detecting lung cancer. This investigation shows helpful results in suggesting the possibility that machine learning techniques could assist to identify lung cancer as exact and timely as possible, providing more successful diagnostic procedures and patient outcomes. The experimental findings show that SVM gives the best result at 95.06%, KNN comes second with a percentage of 86.89.

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Section: Literature Surveymentioning

confidence: 99%

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

2024

ijacsa

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“…Asuntha and Srinivasan 15 employed the GLCM function for bone fracture detection. However, their results indicated that using GLCM as the sole textural feature is insufficient for reliable identification of malignant bone.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach

Shrivastava,

Nag

2023

Surg Innov

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Background: Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification of malignant and healthy bone. Methods: Differentiating between them poses a challenge as they may exhibit similar physical characteristics. The initial step is selecting the optimal edge detection method. Two feature sets are then generated: one with the histogram of oriented gradients (HOG) and one without. Performance evaluation involves two machine learning models: Support Vector Machine (SVM) and Random Forest. Results: Including HOG consistently yields superior results. The SVM model with HOG achieves an F-1 score of 0.92, outperforming the Random Forest model’s .77. This study aims to develop reliable methods for bone cancer classification. The proposed automated method assists surgeons in accurately detecting malignant bone regions using modern image analysis techniques and machine learning models. Incorporating HOG significantly enhances performance, improving differentiation between malignant and healthy bone. Conclusion: Ultimately, this approach supports precise diagnoses and informed treatment decisions for bone cancer patients.

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Lung cancer detection using Adaptive Bilateral Filter (ABF) techniques

Cited by 2 publications

References 0 publications

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

Image Processing-based Performance Evaluation of KNN and SVM Classifiers for Lung Cancer Diagnosis

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach

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