Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Chan, Sophal; Dittakan, Kwankamon; García-Constantino, Matías

doi:10.18517/ijaseit.10.6.8279

Cited by 7 publications

(3 citation statements)

References 47 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Random forests [15], Decision trees [16], Support Vector Machines (SVM) [17], K-nearest Neighbor (KNN) [18], and Neural Networks (NN) [19] are some of the comparison techniques utilised to assess the proposed deep CNN classifier.…”

Section: Comparative Methodsmentioning

confidence: 99%

Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification

Goswami

2023

IJRITCC

View full text Add to dashboard Cite

Millions of people are affected by the disease Knee Osteoarthritis, and the prevalence of the condition is steadily increasing. Knee osteoarthritis has a significant impact on people's lives by generating increased worry, mental health disorders, and physical problems. Early detection of knee osteoarthritis is critical for decreasing disease consequences, and numerous studies are being conducted to classify knee osteoarthritis. In this study, the deep CNN classifier is used to classify knee osteoarthritis, which effectively extracts the features required for disease classification more efficiently. The preprocessing of the data, which is done in three processes such as Circular Fourier Transform, Multivariate Linear Function, and Histogram Equalization, is particularly important in this research since it aids in obtaining more efficient information about the image. The deep CNN classifier's weights and bias deliver better and desired classification results while spending less time and storage. The proposed deep CNN classifier attained the Accuracy of 94.244%, F1 measure of 94.059%, Precision of 94.059%, Recall of 93.586%.

show abstract

Section: Comparative Methodsmentioning

confidence: 99%

Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification

Goswami

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…The medical use of image textures has been reported in the previous studies. Examples are early detection of Alzheimer's disease [ 6 ], dengue fever [ 7 ], osteoarthritis [ 8 , 9 ], and lung cancer [ 10 , 11 ], while research on disease diagnosis based on iris image feature extraction is used to diagnose kidney disease [ 12 ] and heart disease [ 13 ], detect Alzheimer's [ 14 ], detect stomach disorders [ 15 ], and others.…”

Section: Introductionmentioning

confidence: 99%

Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

Hapsari

Miswanto

Rulaningtyas

et al. 2022

International Journal of Biomedical Imaging

View full text Add to dashboard Cite

Iris has specific advantages, which can record all organ conditions, body construction, and psychological disorders. Traces related to the intensity or deviation of organs caused by the disease are recorded systematically and patterned on the iris and its surroundings. The pattern that appears on the iris can be recognized by using image processing techniques. Based on the pattern in the iris image, this paper aims to provide an alternative noninvasive method for the early detection of DM and HC. In this paper, we perform detection based on iris images for two diseases, DM and HC simultaneously, by developing the invariant Haralick feature on quantized images with 256, 128, 64, 32, and 16 gray levels. The feature extraction process does early detection based on iris images. Researchers and scientists have introduced many methods, one of which is the feature extraction of the gray-level co-occurrence matrix (GLCM). Early detection based on the iris is done using the volumetric GLCM development, namely, 3D-GLCM. Based on 3D-GLCM, which is formed at a distance of d = 1 and in the direction of 0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°, it is used to calculate Haralick features and develop Haralick features which are invariant to the number of quantization gray levels. The test results show that the invariant feature with a gray level of 256 has the best identification performance. In dataset I, the accuracy value is 97.92, precision is 96.88, and recall is 95.83, while in dataset II, the accuracy value is 95.83, precision is 89.69, and recall is 91.67. The identification of DM and HC trained on invariant features showed higher accuracy than the original features.

show abstract

“…It has been extremely useful and effective in the planning exercises of radiotherapy procedures. Planning is very relevant in the optimization of medical imaging modalities [3], [4] and also in cancer research and medical imaging-related studies [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Urethane Based Tissue Equivalent Substitute for Phantom Construction: Model Molding, XCOM and MCNPX Studies

Bello¹,

Nor²,

Hassan³

2022

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

The Soft and Lung tissue equivalent substitute (STES and LTES) were developed from urethane PMC121/30 Dry (A and B) of Smooth-On, USA. The part A and B were mixed in the ratio 1:1 and further mixed with calcium carbonate (CaCO3) at a ratio 2:1 by mass. Air moisture was extracted from the mixture for 10minutes. This is the STES and the density after air extraction was 1.04gcm -3 . The LTES was developed by mixing the STES and polystyrene beads at a ratio 10:1 by mass. The density of the LTES was 0.25gcm -3 after air extraction. The STES and the LTES were subjected to compression test for stress-strain analysis. The elemental composition of STES and LTES was achieved using XCOM software with the IUPAC nomenclatures of the source compounds as inputs. The elemental composition obtained was used to modify the lung and the soft tissue material of the AMALE and AFEMALE computational phantom of ORNL. The phantom was subjected to photon exposure (0.06MeV-15.00MeV) using MCNPX Version 27e. The results from MCNPX provided the bremsstrahlung, positron annihilation, and the fluorescence energies that was used to estimate the g-factor. The mass-energy transfer coefficient (μtra/ρ) and the mass-energy absorption coefficient (μen/ρ) were calculated using the values of g-factor, the fluence and the Kerma. The μen/ρ of the tissue-equivalent agrees with the National Institute of Standard values and the ICRU 44. The STES and LTES are technically proper research and teaching models for dose measurements with these results.

show abstract

Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

Cited by 7 publications

References 47 publications

Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification

Deep Convolutional Neural Network Classifier for Effective Knee Osteoarthritis Classification

Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image

Characterization of the Urethane Based Tissue Equivalent Substitute for Phantom Construction: Model Molding, XCOM and MCNPX Studies

Contact Info

Product

Resources

About