{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T21:58:50Z","timestamp":1775167130538,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006701","name":"Umm al-Qura University","doi-asserted-by":"publisher","award":["25UQU4320430GSSR02"],"award-info":[{"award-number":["25UQU4320430GSSR02"]}],"id":[{"id":"10.13039\/501100006701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The Draw-A-Person Intellectual Ability test for children, adolescents, and adults is a widely used tool in psychology for assessing intellectual ability. This test relies on human drawings for initial raw scoring, with the subsequent conversion of data into IQ ranges through manual procedures. However, this manual scoring and IQ assessment process can be time-consuming, particularly for busy psychologists dealing with a high caseload of children and adolescents. Presently, DAP-IQ screening continues to be a manual endeavor conducted by psychologists. The primary objective of our research is to streamline the IQ screening process for psychologists by leveraging deep learning algorithms. In this study, we utilized the DAP-IQ manual to derive IQ measurements and categorized the entire dataset into seven distinct classes: Very Superior, Superior, High Average, Average, Below Average, Significantly Impaired, and Mildly Impaired. The dataset for IQ screening was sourced from primary to high school students aged from 8 to 17, comprising over 1100 sketches, which were subsequently manually classified under the DAP-IQ manual. Subsequently, the manual classified dataset was converted into digital images. To develop the artificial intelligence-based models, various deep learning algorithms were employed, including Convolutional Neural Network (CNN) and state-of-the-art CNN (Transfer Learning) models such as Mobile-Net, Xception, InceptionResNetV2, and InceptionV3. The Mobile-Net model demonstrated remarkable performance, achieving a classification accuracy of 98.68%, surpassing the capabilities of existing methodologies. This research represents a significant step towards expediting and enhancing the IQ screening for psychologists working with diverse age groups.<\/jats:p>","DOI":"10.3390\/bdcc9070164","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T06:49:38Z","timestamp":1750747778000},"page":"164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep Learning-Based Draw-a-Person Intelligence Quotient Screening"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8797-6563","authenticated-orcid":false,"given":"Shafaat","family":"Hussain","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6724-6705","authenticated-orcid":false,"given":"Toqeer","family":"Ehsan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0935-0774","authenticated-orcid":false,"given":"Hassan","family":"Alhuzali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah 24382, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6650-3469","authenticated-orcid":false,"given":"Ali","family":"Al-Laith","sequence":"additional","affiliation":[{"name":"Computer Science Department, Copenhagen University, 2300 Copenhagen, Denmark"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brody, N. (2000). History of theories and measurements of intelligence. Handbook of Intelligence, Cambridge University Press.","DOI":"10.1017\/CBO9780511807947.003"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Spearman, C. (1927). The Measurement of Intelligence, Houghton Mifflin Company.","DOI":"10.1038\/120577a0"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gardner, M.K. (2011). Theories of Intelligence. The Oxford Handbook of School Psychology, Oxford Academic.","DOI":"10.1093\/oxfordhb\/9780195369809.013.0035"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.19030\/ctms.v1i3.5236","article-title":"Spiritual-intelligence\/-quotient","volume":"1","author":"Selman","year":"2005","journal-title":"Coll. Teach. Methods Styles J. (CTMS)"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1037\/h0020410","article-title":"Research with the Wechsler Intelligence Scales for Adults","volume":"66","author":"Guertin","year":"1966","journal-title":"Psychol. Bull."},{"key":"ref_6","unstructured":"Parankimalil, J. (2014). Meaning, nature and characteristics of intelligence. Educationist, Story Teller and Motivator, Available online: https:\/\/johnparankimalil.wordpress.com\/2014\/11\/17\/meaning-nature-and-characteristics-of-intelligence\/."},{"key":"ref_7","unstructured":"Raven, J. (2003). Raven progressive matrices. Handbook of Nonverbal Assessment, Springer."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/07421656.2003.10129396","article-title":"The use of the Naglieri Draw-a-Person test of cognitive development: A study with clinical and research implications for art therapists working with children","volume":"20","author":"Hagood","year":"2003","journal-title":"Art Ther."},{"key":"ref_9","unstructured":"Groth-Marnat, G. (2009). Handbook of Psychological Assessment, John Wiley & Sons."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1001\/jama.1968.03140290082037","article-title":"Psychological evaluation of children\u2019s human figure drawings","volume":"205","author":"Koppitz","year":"1968","journal-title":"JAMA"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1177\/0734282905285249","article-title":"The reliability of scores for the Draw-A-Person intellectual ability test for children, adolescents, and adults","volume":"24","author":"Williams","year":"2006","journal-title":"J. Psychoeduc. Assess."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1037\/0735-7028.31.2.155","article-title":"Current trends in psychological testing of children","volume":"31","author":"Kamphaus","year":"2000","journal-title":"Prof. Psychol. Res. Pract."},{"key":"ref_13","first-page":"329","article-title":"Draw-a-person test as a tool for intelligence screening in primary school children","volume":"32","author":"Khalil","year":"2019","journal-title":"Menoufia Med J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"485","DOI":"10.2466\/03.04.PR0.115c25z8","article-title":"Problems of \u201cDraw-a-Person: A Quantitative Scoring System\u201d (DAP: QSS) as a measure of intelligence","volume":"115","author":"Troncone","year":"2014","journal-title":"Psychol. Rep."},{"key":"ref_15","first-page":"61","article-title":"Differences of emotional intelligence, aggression, and academic achievement among students with different levels of intellectual ability","volume":"19","author":"Mursaleen","year":"2020","journal-title":"Bahria J. Prof. Psychol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Setiawan, I., Yusnitasari, T., Nurhady, H., and Hizviani, N.V. (2020, January 3\u20134). Implementation of convolutional neural network method for classification of Baum Test. Proceedings of the 2020 Fifth International Conference on Informatics and Computing (ICIC), IEEE, Gorontalo, Indonesia.","DOI":"10.1109\/ICIC50835.2020.9288595"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Salar, A.A., Faiyad, H., S\u00f6nmez, E.B., and Hafton, S. (2023, January 19\u201321). Artificial Intelligence Contribution to Art-Therapy using Drawings of the House-Person-Tree Test. Proceedings of the 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IEEE, Tenerife, Canary Islands, Spain.","DOI":"10.1109\/ICECCME57830.2023.10252218"},{"key":"ref_18","unstructured":"Noor, M.N., Nazir, M., Rehman, S., and Tariq, J. (2021, January 18). Sketch-recognition using pre-trained model. Proceedings of the National Conference on Engineering and Computing Technology, Islamabad, Pakistan."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Maliki, I., and Firmansyah, A.R. (2023, January 25). Personality Detection Based on Tree Drawing Using Convolutional Neural Network. Proceedings of the 2023 International Conference on Informatics Engineering, Science & Technology (INCITEST), IEEE, Bandung, Indonesia.","DOI":"10.1109\/INCITEST59455.2023.10396897"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Widiyanto, S., and Abuhasan, J.W. (2020, January 2\u20135). Implementation the convolutional neural network method for classification the draw-A-person test. Proceedings of the 2020 Fifth International Conference on Informatics and Computing (ICIC), IEEE, Bari, Italy.","DOI":"10.1109\/ICIC50835.2020.9288651"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mathew, A., Amudha, P., and Sivakumari, S. (2021). Deep learning techniques: An overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, Springer.","DOI":"10.1007\/978-981-15-3383-9_54"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 13\u201316). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_28","first-page":"1","article-title":"Large scale distributed deep networks","volume":"25","author":"Dean","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_30","first-page":"389","article-title":"Efficient mobilenet architecture as image recognition on mobile and embedded devices","volume":"16","author":"Khasoggi","year":"2019","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fallah, B., and Khotanlou, H. (2016, January 9). Identify human personality parameters based on handwriting using neural network. Proceedings of the 2016 Artificial Intelligence and Robotics (IRANOPEN), IEEE, Qazvin, Iran.","DOI":"10.1109\/RIOS.2016.7529501"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Arya, A., and Manuel, M. (2020, January 25\u201326). Intelligence Quotient Classification from Human MRI Brain Images Using Convolutional Neural Network. Proceedings of the 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), IEEE, Bhimtal, India.","DOI":"10.1109\/CICN49253.2020.9242552"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/7\/164\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:57:32Z","timestamp":1760032652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/7\/164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,24]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["bdcc9070164"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9070164","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,24]]}}}