All Issue

2025 Vol.16, Issue 2 Preview Page

General Article

30 June 2025. pp. 251-267
Abstract
References
1

S. Kittipat and S. Bunritt, Dermatological classification using deep learning of skin image and patient background knowledge. International Journal of Machine Learning and Computing. 9(6) (2019), pp. 862-867.

10.18178/ijmlc.2019.9.6.884
2

V.B. Kumar, S.S. Kumar, and V. Saboo, Dermatological Disease Detection Using Image Processing and Machine Learning. 2016 3rd Int. Conf. Artif. Intell. Pattern Recognition, AIPR. (2016), pp. 88-93.

10.1109/ICAIPR.2016.7585217
3

H. Fujita, The role of IL-22 and Th22 cells in human skin diseases. J. Dermatol. Sci. 72(1) (2013), pp. 3-8.

10.1016/j.jdermsci.2013.04.028
4

L.F. Li, X. Wang, W.J. Hu, N.N. Xiong, Y.X. Du, and B.S. Li, Deep Learning in Skin Disease Image Recognition: A Review. IEEE Access. 8 (2020), pp. 208264-208280.

10.1109/ACCESS.2020.3037258
5

M. Amagai and J.R. Stanley, Desmoglein as a Target in Skin Disease and Beyond. J. Invest. Dermatol. 132(3) (2012), pp. 776-784.

10.1038/jid.2011.390PMC3279627
6

S. Vyas, A. Banerjee, and P. Burlina, Machine learning methods for in vivo skin parameter estimation. Proc. - IEEE Symp. Comput. Med. Syst. (2013), pp. 524-525.

10.1109/CBMS.2013.6627860
7

K. Shibuya, C.D. Mathers, C. Boschi-Pinto, A.D. Lopez, and C.J.L. Murray, Global and regional estimates of cancer mortality and incidence by site: II. Results for the global burden of disease 2000. BMC Cancer. 2 (2002), pp. 1-26.

10.1186/1471-2407-2-37PMC149364
8

C. Karimkhani, R.P. Dellavalle, L.E. Coffeng, C. Flohr, R.J. Hay, S.M. Langan, E.O. Nsoesie, A.J. Ferrari, H.E. Erskine, J.I. Silverberg, T. Vos, and M. Naghavi, Global Skin Disease Morbidity and Mortality: An Update From the Global Burden of Disease Study 2013. JAMA Dermatology. 153(5) (2017), pp. 406-412.

10.1001/jamadermatol.2016.5538PMC5817488
9

H.I. Suk and D. Shen, Deep Learning-Based Feature Representation for AD/MCI Classification, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8150 LNCS, (2013), pp. 583-590.

10.1007/978-3-642-40763-5_72PMC4029347
10

N.C.F. Codella, Q.B. Nguyen, S. Pankanti, D.A. Gutman, B. Helba, and A.C. Halpern, Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J. Res. Dev. 61(4) (2017), pp. 5-1.

10.1147/JRD.2017.2708299
11

N. Codella, J. Cai, M. Abedini, R. Garnavi, A. Halpern, and J.R. Smith, Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 9352 (2015), pp. 118-126.

10.1007/978-3-319-24888-2_15
12

S.V. Deo, H. Sidhartha, S. Nootan, K.K. Sunil, K. Madhabananda, and S. Atul, Surgical management of skin cancers: Experience from a regional cancer centre in North India. Indian J. Cancer. 42(3) (2005), pp. 145-150.

10.4103/0019-509X.17059
13

R. Sumithra, M. Suhil, and D.S. Guru, Segmentation and Classification of Skin Lesions for Disease Diagnosis. Procedia Comput. Sci. 45 (2015), pp. 76-85.

10.1016/j.procs.2015.03.090
14

A.A. Elngar, R. Kumar, A. Hayat, and P. Churi, Intelligent System for Skin Disease Prediction using Machine Learning. J. Phys. Conf. Ser. 1998(1) (2021), 012037.

10.1088/1742-6596/1998/1/012037
15

M. Usama, M.A. Naeem, and F. Mirza, Multi-Class Skin Lesions Classification Using Deep Features. Sensors. 22(21) (2022), 8311.

10.3390/s22218311PMC9658979
16

E. Agu, P. Pedersen, D. Strong, B. Tulu, Q. He, L. Wang, and Y. Li, The smartphone as a medical device: Assessing enablers, benefits and challenges. 2013 IEEE Int. Work. Internet-of-Things Netw. Control. IoT-NC. (2013), pp. 48-52.

10.1109/IoT-NC.2013.6694053
17

P. Pouladzadeh, P. Kuhad, S.V.B. Peddi, A. Yassine, and S. Shirmohammadi, Food calorie measurement using deep learning neural network. Conf. Rec. - IEEE Instrum. Meas. Technol. Conf. 2016 (2016).

10.1109/I2MTC.2016.7520547
18

E. Roger, L. Torlay, J. Gardette, C. Mosca, S. Banjac, L. Minotti, P. Kahane, and M. Baciu, A machine learning approach to explore cognitive signatures in patients with temporo-mesial epilepsy. Neuropsychologia. 142 (2020), 107455.

10.1016/j.neuropsychologia.2020.107455
19

P.S. Kohli and S. Arora, Application of machine learning in disease prediction, Dec. (2018).

10.1109/CCAA.2018.8777449
20

G.S. Vennila, L.P. Suresh, and K.L. Shunmuganathan, Dermoscopic image segmentation and classification using machine learning algorithms. 2012 Int. Conf. Comput. Electron. Electr. Technol. ICCEET. (2012), pp. 1122-1127.

10.1109/ICCEET.2012.6203834
21

H. Li, Y. Pan, J. Zhao, and L. Zhang, Skin disease diagnosis with deep learning: A review. Neurocomputing. 464 (2021), pp. 364-393.

10.1016/j.neucom.2021.08.096
22

M. K.K, K. Sankaranarayanan, and P. Seena, Prediction of Different Dermatological Conditions Using Naïve Bayesian Classification. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(1) (2014), 2277.

23

R.B. Oliveira, M.E. Filho, Z. Ma, J.P. Papa, A.S. Pereira, and J.M.R.S. Tavares, Computational methods for the image segmentation of pigmented skin lesions: A review. Comput. Methods Programs Biomed. 131 (2016), pp. 127-141.

10.1016/j.cmpb.2016.03.032
24

G. Sahu and R. Kumar Khare, Decision Tree Classification based Decision Support System for Derma Disease. Int. J. Comput. Appl. 94(17) (2014), pp. 975-8887.

10.5120/16451-6171
25

V.R. Balaji, S.T. Suganthi, R. Rajadevi, V. Krishna Kumar, B. Saravana Balaji, and S. Pandiyan, Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier. Meas. J. Int. Meas. Confed. 163 (2020), 107922.

10.1016/j.measurement.2020.107922
26

S.P. Singh, L. Wang, S. Gupta, H. Goli, P. Padmanabhan, and B. Gulyás, 3D Deep Learning on Medical Images: A Review. Sensors. 20(18) (2020), 5097.

10.3390/s20185097PMC7570704
27

M.R. Hasan, M.I. Fatemi, M. Monirujjaman Khan, M. Kaur, and A. Zaguia, Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks. J. Healthc. Eng. (2021).

10.1155/2021/5895156PMC8684510
28

S. Srivastava, S. Soman, A. Rai, and P.K. Srivastava, Deep learning for health informatics: Recent trends and future directions. 2017 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2017. 3 (2017), pp. 1665-1670.

10.1109/ICACCI.2017.8126082
29

A. Alrabiah, M. Alduailij, and M. Crane, Computer-based Approach to Detect Wrinkles and Suggest Facial Fillers. IJACSA, Int. J. Adv. Comput. Sci. Appl. 10(9) (2019).

10.14569/IJACSA.2019.0100941
30

S.A. Aldera, M. Tahar, and B. Othman, A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques. IJACSA, Int. J. Adv. Comput. Sci. Appl. 13(5) (2022), 2022.

10.14569/IJACSA.2022.0130531
31

N. Hameed, A. Shabut, and M.A. Hossain, A Computer-Aided diagnosis system for classifying prominent skin lesions using machine learning. 2018 10th Comput. Sci. Electron. Eng. Conf. CEEC 2018 - Proc. (2019), pp. 186-191.

10.1109/CEEC.2018.8674183
32

J.L. Seixas, S. Barbon, and R.G. Mantovani, Pattern recognition of lower member skin ulcers in medical images with machine learning algorithms. Proc. - IEEE Symp. Comput. Med. Syst. (2015), pp. 50-53.

10.1109/CBMS.2015.48
33

V. Gautam, V. Gautam, N.K. Trivedi, A. Anand, R. Tiwari, A. Zaguia, D. Koundal, and S. Jain, Early Skin Disease Identification Using eep Neural Network. Comput. Syst. Sci. Eng. 44(3) (2022), pp. 2259-2275.

10.32604/csse.2023.026358
34

S. Chatterjee, D. Dey, and S. Munshi, Mathematical morphology aided shape, texture and colour feature extraction from skin lesion for identification of malignant melanoma. 2015 Int. Conf. Cond. Assess. Tech. Electr. Syst. CATCON 2015 - Proc. (2015), pp. 200-203.

10.1109/CATCON.2015.7449534
35

G. Zhang, L. Zhong, Y. Huang, and Y. Zhang, A histopathological image feature representation method based on deep learning. Proc. - 2015 7th Int. Conf. Inf. Technol. Med. Educ. ITME 2015. (2016), pp. 13-17.

10.1109/ITME.2015.34
36

S. Putatunda, A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease. Proc. CONECCT 2020 - 6th IEEE Int. Conf. Electron. Comput. Commun. Technol., Jul. (2020).

10.1109/CONECCT50063.2020.9198447
37

L. Alzubaidi, J. Zhang, A.J. Humaidi, A.A. Dujaili, Y. Duan, O.A. Shamma, J. Santamaria, M.A. Fadhel, M.A. Amidie, and L. Farhan, Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data. 8(1) (2021), pp. 1-74.

10.1186/s40537-021-00444-8PMC8010506
38

U. Leiter, U. Keim, and C. Garbe, Epidemiology of skin cancer: Update 2019. Adv. Exp. Med. Biol. 1268 (2020), pp. 123-139.

10.1007/978-3-030-46227-7_6
39

H.M. Gloster and K. Neal, Skin cancer in skin of color. J. Am. Acad. Dermatol. 55(5) (2006), pp. 741-760.

10.1016/j.jaad.2005.08.063
40

Z. Apalla, A. Lallas, E. Sotiriou, E. Lazaridou, and D. Ioannides, Epidemiological trends in skin cancer What Does the Future Hold. Dermatol. Pract. Concept. 7(2) (2017), pp. 1-6.

10.5826/dpc.0702a01PMC5424654
41

N. Alfed and F. Khelifi, Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images. Expert Syst. Appl. 90 (2017), pp. 101-110.

10.1016/j.eswa.2017.08.010
42

C. Kwon and Y. Ahn, Critical views on AI (Artificial Intelligence) in building design. Int. J. of Sustai. Build. Techn. and Urban Dev. 15(2) (2024), pp. 240-246.

43

M. Gowthaman and S. Sathpriya, A Comprehensive review of analysis of geo-cell reinforced grit bed on footings. Int. J. of Sustai. Build. Techn. and Urban Dev. 15(4) (2024), pp. 465-483.

44

S.P. Burger and M. Luke, Business models for distributed energy resources: A review and empirical analysis. Energy Policy. 109 (2015), pp. 230-248.

10.1016/j.enpol.2017.07.007
45

H.L. Howe, P.A. Wingo, M.J. Thun, L.A.G. Ries, H.M. Rosenberg, E.G. Feigal, and B.K. Edwards, Annual Report to the Nation on the Status of Cancer (1973 Through 1998), Featuring Cancers With Recent Increasing Trends. JNCI J. Natl. Cancer Inst. 93(11) (2001), pp. 824-842.

10.1093/jnci/93.11.824
46

A. Haddad and S.A. Hameed, Image Analysis Model for Skin Disease Detection: Framework. in Proceedings of the 2018 7th International Conference on Computer and Communication Engineering, ICCCE. (2018), pp. 280-283.

10.1109/ICCCE.2018.8539270
47

T. Saba, Region Extraction and Classification of Skin Cancer: A Heterogeneous framework of Deep CNN Features Fusion and Reduction, (2019).

10.1007/s10916-019-1413-3
48

M.S. Kolkur, D.R. Kalbande, and V. Kharkar, Machine Learning Approaches to Multi-Class Human Skin Disease Detection. Int. J. Comput. Intell. Res. 14(1) (2018), pp. 29-39.

49

N.S. Alkolifi Alenezi, A Method Of Skin Disease Detection Using Image Processing And Machine Learning. Procedia Comput. Sci. 163 (2019), pp. 85-92.

10.1016/j.procs.2019.12.090
50

S. Dash, S.K. Shakyawar, M. Sharma, and S. Kaushik, Big data in healthcare: management, analysis and future prospects. J. Big Data, 6(1) (2019).

10.1186/s40537-019-0217-0
51

R. Bauder, T.M. Khoshgoftaar, and N. Seliya, A survey on the state of healthcare upcoding fraud analysis and detection. Heal. Serv. Outcomes Res. Methodol. 17(1) (2017), pp. 31-55.

10.1007/s10742-016-0154-8
52

Z. Khurshid, A. De Brún, G. Moore, and E. McAuliffe, Virtual adaptation of traditional healthcare quality improvement training in response to COVID-19: a rapid narrative review. Hum. Resour. Health. 18(1) (2020), pp. 1-18.

10.1186/s12960-020-00527-2PMC7594275
53

K. Jinoos, A. Mohammad, A. Masood, and D. Ali, Ethnobotanical Study of Medicinal Plants used in Skin Diseases in the Area Alamut-Qazvin. Iran, J. Med. Plants. 18(72) (2019), pp. 121-132.

10.29252/jmp.4.72.S12.121
54

M. Adel Ebaid, A framework for implementing biophilic design in cancer healthcare spaces to enhance patients's experience. Int. J. of Sustai. Build. Techn. and Urban Dev. 14(2) (2023), pp. 229-246.

55

N. Deshmukh, Low-Cost Device Prototype for Automatic Medical Diagnosis Using Deep Learning Methods. 2018 9th IEEE Annu. Ubiquitous Comput. Electron. Mob. Commun. Conf. UEMCON. (2018), pp. 695-699.

10.1109/UEMCON.2018.8796551
56

M. Dildar, S. Akram, M. Irfan, H.U. Khan, M. Ramzan, A.R. Mahmood, S.A. Alsaiari, A.H.M. Saeed, M.O. Alraddadi, and M.H. Mahnashi, Skin Cancer Detection: A Review Using Deep Learning Techniques, 2021.

10.3390/ijerph18105479PMC8160886
57

M. Arora, A. Prakash, S. Dixit, A. Mittal, and S. Singh, A critical review of HR analytics: visualization and bibliometric analysis approach. Inf. Discov. Deliv. (2022).

10.1108/IDD-05-2022-0038
58

S. Harnal, G. Sharma, S. Malik, G. Kaur, S. Khurana, P. Kaur, S. Simaiya, and D. Bagga, Bibliometric Mapping of Trends, Applications and Challenges of Artificial Intelligence in Smart Cities. EAI Endorsed Trans. Scalable Inf. Syst. 9(4) (2022), pp. e8-e8.

10.4108/eetsis.vi.489
59

B.S. dos Santos, M.T.A. Steiner, A.T. Fenerich, and R.H.P. Lima, Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput. Ind. Eng. 138 (2019), pp. 106120.

10.1016/j.cie.2019.106120
60

F. Suhail, M. Adel, M. Al-Emran, and K. Shaalan, A Bibliometric Analysis on the Role of Artificial Intelligence in Healthcare. Stud. Comput. Intell. 1024 (2022), pp. 1-14.

10.1007/978-981-19-1076-0_1
61

X. Pei, K. Zuo, Y. Li, and Z. Pang, A Review of the Application of Multi-modal Deep Learning in Medicine: Bibliometrics and Future Directions. Int. J. Comput. Intell. Syst. 16(1) (2023), pp. 1-20.

10.1007/s44196-023-00225-6
62

D. Kim, Y. Chae, H.J. Park, and I.S. Lee, A bibliometric analysis of atopic dermatitis research over the past three decades and future perspectives. Healthc. 9(12) (2021), pp. 1-14.

10.3390/healthcare9121749PMC8702046
63

N.J. van Eck and L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 84(2) (2010), pp. 523-538.

10.1007/s11192-009-0146-3PMC2883932
64

K. Mahadevan and S. Joshi, Omnichannel retailing: a bibliometric and network visualization analysis. Benchmarking. 29(4) (2022), pp. 1113-1136.

10.1108/BIJ-12-2020-0622
65

X. Xiong, X. Guo, and Y. Wang, Modeling of Human Skin by the Use of Deep Learning. (2021).

10.1155/2021/5531585
66

N.I.A. Dabowsa, N.M. Amaitik, A.M. Maatuk, and S.A. Aljawarneh, A hybrid intelligent system for skin disease diagnosis. Proc. 2017 Int. Conf. Eng. Technol. ICET 2017. 2018 (2017), pp. 1-6.

10.1109/ICEngTechnol.2017.8308157
67

R. Parisi, I.Y.K. Iskandar, E. Kontopantelis, M. Augustin, C.E.M. Griffiths, and D.M. Ashcroft, National, regional, and worldwide epidemiology of psoriasis: Systematic analysis and modelling study. BMJ. 369 (2020).

10.1136/bmj.m1590PMC7254147
68

H. Williams, C. Robertson, A. Stewart, N.A. Khaled, G. Anabwani, R. Anderson, I. Asher, R. Beasley, B. Björkstén, M. Burr, T. Clayton, J. Crane, P. Ellwood, U. Keil, C. Lai, J. Mallol, F. Martinez, E. Mitchell, S. Montefort, N. Pearce, and S.K. Weiland, Worldwide variations in the prevalence of symptoms of atopic eczema in the international study of asthma and allergies in childhood. J. Allergy Clin. Immunol. 103(1) (1999), pp. 125-138.

10.1016/S0091-6749(99)70536-1
69

S.S. Mohammed and J.M. Al-Tuwaijari, Skin Disease Classification System Based on Machine Learning Technique: A Survey. IOP Conf. Ser. Mater. Sci. Eng. 1076(1) (2021), 012045.

10.1088/1757-899X/1076/1/012045
70

D.N. Jaysawal, Rural Health System in India: A Review. SSRN Electron. J. Feb. (2015).

10.2139/ssrn.2608313
71

D.A. Gavrilov, A.V. Melerzanov, N.N. Shchelkunov, and E.I. Zakirov, Use of Neural Network-Based Deep Learning Techniques for the Diagnostics of Skin Diseases. Biomed. Eng. (NY). 52(5) (2019), pp. 348-352.

10.1007/s10527-019-09845-9
72

P. Tschandl, C. Rosendahl, and H. Kittler, Data descriptor: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data. 5 (2018), pp. 1-9.

10.1038/sdata.2018.161PMC6091241
73

M.E. Celebi, H.A. Kingravi, B. Uddin, H. Iyatomi, Y.A. Aslandogan, W.V. Stoecker, and R.H. Mosset, A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6) (2007), pp. 362-373.

10.1016/j.compmedimag.2007.01.003PMC3192405
74

K. Zafar, S.O. Gilani, A. Waris, A. Ahmed, M. Jamil, M.N. Khan, and A.S. Kashif, Skin lesion segmentation from dermoscopic images using convolutional neural network. Sensors (Switzerland). 20(6) (2020), pp. 1-14.

10.3390/s20061601PMC7147706
75

B.K. Yoon, J.A. Jackman, E.R. Valle-González, and N.J. Cho, Antibacterial free fatty acids and monoglycerides: Biological activities, experimental testing, and therapeutic applications. Int. J. Mol. Sci. 19(8) (2018).

10.3390/ijms19041114PMC5979495
76

P.N. Srinivasu, J.G. Sivasai, M.F. Ijaz, A.K. Bhoi, W. Kim, and J.J. Kang, Networks with MobileNet V2 and LSTM. (2021), pp. 1-27.

77

N.J. van Eck and L. Waltman, Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics. 111(2) (2017), pp. 1053-1070.

10.1007/s11192-017-2300-7PMC5400793
78

D. Kumar, A. Verma, M. Kumar, V. Mauyra, and A. Mishra, Utilizing machine learning for the assessment of mosquito repellent effectiveness and decision support in product selection. Int. J. of Sustai. Build. Techno. and Urban Dev. 14(4) (2023), pp. 519-533.

Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 16
  • No :2
  • Pages :251-267
  • Received Date : 2025-05-14
  • Accepted Date : 2025-06-08
Journal Informaiton International Journal of Sustainable Building Technology and Urban Development International Journal of Sustainable Building Technology and Urban Development
  • scopus
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
  • isc
Journal Informaiton Journal Informaiton - close