Hotspot Detection Pada Citra Termal Wajah Anak Autis  Dan Normal Berbasis Otsu Thresholding

Zharifah Muthiah; Yayang Hafifah; Melinda Melinda; Ramzi Adriman

doi:10.60076/ijstech.v2i1.442

Authors

Zharifah Muthiah Universitas Syiah Kuala
Yayang Hafifah Universitas Syiah Kuala
Melinda Melinda Universitas Syiah Kuala
Ramzi Adriman Universitas Syiah Kuala

DOI:

https://doi.org/10.60076/ijstech.v2i1.442

Keywords:

Hotspot Detection, Autis, Citra Wajah Termal, Otsu Thresholding

Abstract

Gangguan Spektrum Autisme (ASD) adalah kelainan neurologis yang memengaruhi keterampilan komunikasi penting untuk kehidupan sehari-hari dan sering kali menimbulkan kesulitan dalam situasi sosial. Saat ini, diagnosis ASD masih sangat bergantung pada metode manusia dan kekurangan tanda-tanda biologis yang pasti. Diagnosis dini ASD memiliki dampak positif yang signifikan, terutama pada anak-anak. Teknik deep learning, khususnya dalam analisis citra medis wajah, telah menjadi fokus penelitian baru dalam deteksi ASD. Penggunaan citra termal sebagai metode pasif untuk menganalisis sinyal fisiologis terkait dengan ASD telah diusulkan. Dalam penelitian sebelumnya, telah dikembangkan model deep learning untuk mengklasifikasi wajah anak autis menggunakan citra termal, dengan data mentah 17 wajah termal autis dan 17 wajah termal anak normal. Namun, penelitian tersebut tidak memberikan informasi mengenai perbedaan secara signifikan antara citra termal wajah anak autis dan normal, dan hanya menggunakan arsitektur CNN secara umum. Penelitian ini bertujuan untuk mengisi celah tersebut dengan menganalisis perbedaan data kelas anak autis dan normal menggunakan Otsu Thresholding Hotspot Detection. Hasil Hotspot Detection menggunakan metode Otsu Thresholding menunjukkan bahwa citra kelompok anak autis adalah sebesar 99.466059, sedangkan untuk kelompok anak normal adalah sebesar 88.850546, mengindikasikan perbedaan yang signifikan antara kedua kelompok. Dengan demikian, dataset citra termal anak autis menunjukkan nilai hotspot detection yang lebih tinggi dibandingkan dengan anak normal.

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References

Jones, W., Klaiman, C., Richardson, S., Lambha, M., Reid, M., Hamner, T., Beacham, C., Lewis, P., Paredes, J., Edwards, L., Marrus, N., Constantino, J.N., Shultz, S. And Klin, A., 2023. Development And Replication Of Objective Measurements Of Social Visual Engagement To Aid In Early Diagnosis And Assessment Of Autism. Jama Network Open, 6(9), P.E2330145.

Perinelli, M.G. And Cloherty, M., 2023. Identification Of Autism In Cognitively Able Adults With Epilepsy: A Narrative Review And Discussion Of Available Screening And Diagnostic Tools. Seizure, [Online] 104(June 2022), Pp.6–11.

Ahsan, M.M., Luna, S.A. And Siddique, Z., 2022. Machine-Learning-Based Disease Diagnosis: A Comprehensive Review. Healthcare (Switzerland), 10(3), Pp.1–30.

Alam, M.S., Rashid, M.M., Faizabadi, A.R., Firdaus, H., Zaki, M., Alam, T.E., Ali, S., Gupta, K.D. And Ahsan, M., 2023. Technologies Efficient Deep Learning-Based Data-Centric Approach For Autism Spectrum Disorder Diagnosis From Facial Images Using. Pp.1–27.

Rusli, N., Sidek, S.N., Yusof, H.M., Ishak, N.I., Khalid, M. And Dzulkarnain, A.A.A., 2020. Implementation Of Wavelet Analysis On Thermal Images For Affective States Recognition Of Children With Autism Spectrum Disorder. Ieee Access, 8, Pp.120818–120834.

Ganesh, K., Umapathy, S. And Thanaraj Krishnan, P., 2021. Deep Learning Techniques For Automated Detection Of Autism Spectrum Disorder Based On Thermal Imaging. Proceedings Of The Institution Of Mechanical Engineers, Part H: Journal Of Engineering In Medicine, 235(10), Pp.1113–1127.

Beary, M., Hadsell, A., Messersmith, R. And Hosseini, M.-P., 2020. Diagnosis Of Autism In Children Using Facial Analysis And Deep Learning. [Online] Tersedia Di : .

Tamilarasi, F.C. And Shanmugam, J., 2020. Convolutional Neural Network Based Autism Classification. Proceedings Of The 5th International Conference On Communication And Electronics Systems, Icces 2020, (Icces), Pp.1208–1212

Melinda, Ahmadiar, Maulisa Oktiana, M.S., 2023. A Novel Autism Spectrum Disorder Children Dataset Based On Thermal Imaging.

Sharma, A.K., Nandal, A., Dhaka, A., Polat, K., Alwadie, R., Alenezi, F. And Alhudhaif, A., 2023. Hog Transformation Based Feature Extraction Framework In Modified Resnet50 Model For Brain Tumor Detection. Biomedical Signal Processing And Control, [Online] 84(October 2022), P.104737.

Aggarwal, A.K., 2022. Learning Texture Features From Glcm For Classification Of Brain Tumor Mri Images Using Random Forest Classifier. Wseas Transactions On Signal Processing, 18(April), Pp.60–63

Afifah, A.N.N., Indrabayu, Suyuti, A. And Syafaruddin, 2020. Hotspot Detection In Photovoltaic Module Using Otsu Thresholding Method. 2020 Ieee International Conference On Communication, Networks And Satellite, Comnetsat 2020 - Proceedings, (December), Pp.408–412.

Al-Rahlawee, A.T.H. And Rahebi, J., 2021. Multilevel Thresholding Of Images With Improved Otsu Thresholding By Black Widow Optimization Algorithm. Multimedia Tools And Applications, 80(18), Pp.28217–28243. Https://Doi.Org/10.1007/S11042-021-10860-W.

Rozy, A., 2023. Jite ( Journal Of Informatics And Telecommunication Engineering ) Diagnosing Achalasia. 7(July), Pp.308–316.

Elshoky, B.R.G., Younis, E.M.G., Ali, A.A. And Ibrahim, O.A.S., 2022. Comparing Automated And Non-Automated Machine Learning For Autism Spectrum Disorders Classification Using Facial Images. Etri Journal, 44(4), Pp.613–623.

Sumari, A.D.W., Marwani, P.I. And Syulistyo, A.R., 2021. Klasifikasi Mutu Telur Burung Puyuh Berdasarkan Warna Dan Tekstur Menggunakan Metode K- Nearest Neighbor ( Knn ) Dan Fusi Classification Of The Quality Quail Eggs Based On Color And Texture Using K-Nearest Neighbor ( Knn ) Method And Information. Jurnal Teknologi Informasi Dan Ilmu Komputer (Jtik), 8(5), Pp.1019–1028.

Engineering, I. And Engineering, I., 2022. Relevance Of Thermal Imaging And Respiration Signals In Recognizing Human Emotions. 7(1), Pp.5166–5175.

Mishra, S., Suman, S.K. And Roy, L.B., 2024. Automated Road Crack Classification Using A Novel Forest Optimization Algorithm For Otsu Thresholding And Hybrid Feature Extraction. International Journal Of Advanced Technology And Engineering Exploration, 11(111), Pp.219–242.