Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods

Firdaus, Qurina; Sigit, Riyanto; Harsono, Tri; Anwar, Anwar

doi:10.1109/ies50839.2020.9231663

Cited by 25 publications

(7 citation statements)

References 10 publications

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“…Based on the outcomes, the KNN technique achieved the lowest recall of 92%. Hence, it is concluded that, the support vector machine accurately The wiener filter and ANN technique for lung cancer detection had a detection accuracy of 94%, the super pixel density-based region approach and SVM classifier [16] for lung cancer detection had an accuracy of 88.23%, and the GLCM and SVM method had an accuracy of 83.33% [26]. All hybrid methods are tested on the same dataset in the proposed research work.…”

Section: Accuracy=(tn+tp)/(tn+fn+tp+fp)mentioning

confidence: 94%

“…The research method cited in [26] uses GLCM and the SVM methods and achieved 83.33% of accuracy The proposed technique compares algorithms in each stage, uses standard GLCM for texture feature extraction, and uses best suited several hybrid machine learning techniques for accurate lung tumor classification by comparing each of the optimized classifiers using an accuracy, a recall, precision, and F1-score, thereby yielding an accuracy of 99.32% for detection of lung cancer more accurately.…”

Section: Accuracy=(tn+tp)/(tn+fn+tp+fp)mentioning

confidence: 99%

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Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Shankara

Hariprasad

Latha

2023

IJECE

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<span lang="EN-US">Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.</span>

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Section: Accuracy=(tn+tp)/(tn+fn+tp+fp)mentioning

confidence: 94%

Section: Accuracy=(tn+tp)/(tn+fn+tp+fp)mentioning

confidence: 99%

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Shankara

Hariprasad

Latha

2023

IJECE

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“…Penelitian ini mengimplementasikan metode Gray Level Cooccurence Matrix (GLCM) untuk tahap klasifikasi dan ekstrasi fitur pada sistem berdasarksn ciri tekstur. Berdasarkan uraian di atas, maka pada penelitian ini diusulkan suatu sistem yang dapat mendeteksi parasit malaria pada sel dara manusia degnan menerapkan metode Gray Level Co-occurence Matrix (GLCM) untuk mengkalsifikasi plasmodium penyebab penyakit malaria dalam sel darah merah manusia [8].…”

Section: Pendahuluanunclassified

Deteksi Parasit Malaria Menggunakan Metode Gray Level Co-Occurance Matrix (GLCM)

Mukti,

Octariana,

Sukmana

et al. 2024

Stains

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Malaria adalah salah satu penyakit menular paling mematikan di dunia. Deteksi dini malaria sangat penting untuk mencegah penyebaran penyakit ini. Tradisionalnya, deteksi malaria dilakukan dengan pemeriksaan mikroskopis darah. Namun, metode ini membutuhkan waktu dan usaha yang relatif lama. Dalam penelitian ini, deteksi parasit malaria dilakukan menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Metode GLCM digunakan untuk mengekstraksi fitur tekstur dari gambar sel darah merah yang terinfeksi malaria. Fitur tekstur ini kemudian digunakan untuk melatih model klasifikasi. Hasil penelitian menunjukkan bahwa metode GLCM dapat digunakan untuk mendeteksi parasit malaria dengan akurasi 89%. Studi ini menyarankan bahwa metode GLCM memiliki potensi sebagai metode deteksi malaria yang lebih cepat dan akurat.

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“…These include Wavelet Transform, NL-means filters, multiple filters such as average, Gaussian, log, median, and Wiener filters, as well as the Gaussian filter. [7,8,9,10]. Implemented in this method are image processing and machine learning approaches for predicting and distinguishing between tumour and non-tumour forms based on CT scan images.…”

Section: Related Workmentioning

confidence: 99%

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

Lata L. Ragha

2024

jes

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Lung cancer is a critical global health concern, necessitating precise early diagnosis and intervention for better patient outcomes. Computed Tomography (CT) scans are pivotal in lung cancer detection, and leveraging advanced technology is crucial. This study introduces "DLCTLungDetectNet," a Convolutional Neural Network (CNN) based deep learning framework, with a focus on early lung cancer detection using CT scan images. The core innovation lies in the integration of the robust "FusionNet," a hybrid model amalgamating feature from ResNet50 and InceptionV3. We conduct a comprehensive comparative analysis, showcasing the superior performance of DLCTLungDetectNet over established architectures such as VGG16, VGG19, and Inception v3. Rigorous evaluation based on standard metrics substantiates DLCTLungDetectNet's high accuracy, precision, Area Under Curve (AUC), and F1 score. This research not only highlights the potential of deep learning in enhancing lung cancer diagnosis but also establishes a benchmark, showcasing the efficacy of the FusionNet hybrid model for achieving superior accuracy in automated lung tumor detection.

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Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods

Cited by 25 publications

References 10 publications

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging

Deteksi Parasit Malaria Menggunakan Metode Gray Level Co-Occurance Matrix (GLCM)

DLCT LUNG Detect Net: Leveraging Deep Learning for Lung Tumor Detection in CT scans

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