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All Journal INDONESIAN JOURNAL OF APPLIED PHYSICS SEMAR
Mohtar Yunianto
Physics Department, Universitas Sebelas Maret
Humanities Astronomy Education Energy Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Physics Social Sciences

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Journal : INDONESIAN JOURNAL OF APPLIED PHYSICS

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Lung Cancer Detection Using a Modified Convolutional Neural Network (CNN) Cari Cari; Mohtar Yunianto; Aisyah Ajibah Rahmah
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 1 (2024): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i1.77032

Abstract

Image processing is used to classify lung images with malignant or normal nodules. The Convolutional Neural Network (CNN) method is often used to classify images. This study uses a modified CNN architecture with various layers, filters, batch size, dropout, and epoch values. Variations were made to determine the best accuracy value and reduce the overfitting value of the proposed CNN architecture. This study implements the method using the Keras library with the Python programming language. The data is in the form of CT-Scan images of lung cancer and normal lungs. The results of several experiments from the proposed model produce an accuracy value of 95% using three layers, 128 filters on the first layer, 256 on the second layer, and 512 filters on the third layer, then with 32 batch sizes, 0.5 dropout.
Using Decision Tree With First and Second-Order Statistical Feature Extraction for Classification of Lung Cancer Mohtar Yunianto; Rizka Dewi Meilina; Esti Suryani
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 14, No 2 (2024): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v14i2.87676

Abstract

The classification of CT-Scan images on images with lung cancer and normal lung has been done by improving the image quality of the median and Gabor filters, extraction of first and second-order statistical features, and decision tree classification. The data used comes from LIDC-IDRI as much as 100 training data and 40 test data. The median filter removes noise without removing edges in the image. A Gabor filter is used to facilitate texture analysis on the image. At the feature extraction stage, statistical variations of the first order, second order statistics and the merging of first and second-order statistics. The best results obtained at the testing stage are program designs with variations of feature extraction combining first and second-order statistics. The level of accuracy obtained is 97.5%, with a sensitivity of 100% and a specificity of 95%.
Lung Cancer Classification using Gray-Level Co-Occurrence Matrix Feature Extraction and Forward Selection Feature Selection based on the K-Nearest Neighbor Algorithm Soeparmi Soeparmi; Mohtar Yunianto; Lukmaniyah Rizky Amalia
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 1 (2025): April
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i1.90378

Abstract

In diagnosing lung cancer, the medical imaging team manually identifies CT-scan images of the lungs. This identification process makes it difficult for the medical imaging team to differentiate between lung cancer and normal images. This is because there is noise in the image, which reduces the image quality, so image processing must reduce the noise. This study used median and Gaussian filters, Otsu thresholding segmentation, GLCM feature extraction, forward selection, and k-nearest Neighbor classification. The research results show that of the 22 statistical features extracted, only 16 were selected for characterizing image classification. The image datasets used are 900 image data sets for program training and 100 image data sets for program testing. With a dataset of 100 image data sets, the level of diagnostic accuracy without forward selection (22 GLCM features) was 81.67%, while the diagnostic accuracy using forward selection (16 GLCM features) was 93.22% with a sensitivity of 92.25% and specificity is 94.46%.
Co-Authors Aisyah Ajibah Rahmah Anwar, Fuad Cari Cari Darmanto Darmanto Esti Suryani Fendi Aji Purnomo Lukmaniyah Rizky Amalia Rizka Dewi Meilina Soeparmi Soeparmi Suparmi Suparmi