168 related articles for article (PubMed ID: 25311811)
21. Toward the discrimination of early melanoma from common and dysplastic nevus using fiber optic diffuse reflectance spectroscopy.
Murphy BW; Webster RJ; Turlach BA; Quirk CJ; Clay CD; Heenan PJ; Sampson DD
J Biomed Opt; 2005; 10(6):064020. PubMed ID: 16409085
[TBL] [Abstract][Full Text] [Related]
22. Enhanced classification of malignant melanoma lesions via the integration of physiological features from dermatological photographs.
Haider S; Cho D; Amelard R; Wong A; Clausi DA
Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():6455-8. PubMed ID: 25571474
[TBL] [Abstract][Full Text] [Related]
23. Automated melanoma detection: multispectral imaging and neural network approach for classification.
Tomatis S; Bono A; Bartoli C; Carrara M; Lualdi M; Tragni G; Marchesini R
Med Phys; 2003 Feb; 30(2):212-21. PubMed ID: 12607839
[TBL] [Abstract][Full Text] [Related]
24. Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network.
Binder M; Kittler H; Seeber A; Steiner A; Pehamberger H; Wolff K
Melanoma Res; 1998 Jun; 8(3):261-6. PubMed ID: 9664148
[TBL] [Abstract][Full Text] [Related]
25. An Efficient Melanoma Diagnosis Approach Using Integrated HMF Multi-Atlas Map Based Segmentation.
Roja Ramani D; Ranjani SS
J Med Syst; 2019 Jun; 43(7):225. PubMed ID: 31190229
[TBL] [Abstract][Full Text] [Related]
26. Detection of melanoma from dermoscopic images of naevi acquired under uncontrolled conditions.
Tenenhaus A; Nkengne A; Horn JF; Serruys C; Giron A; Fertil B
Skin Res Technol; 2010 Feb; 16(1):85-97. PubMed ID: 20384887
[TBL] [Abstract][Full Text] [Related]
27. Skin lesion classification with ensembles of deep convolutional neural networks.
Harangi B
J Biomed Inform; 2018 Oct; 86():25-32. PubMed ID: 30103029
[TBL] [Abstract][Full Text] [Related]
28. Melanoma recognition in dermoscopy images using lesion's peripheral region information.
Tajeddin NZ; Asl BM
Comput Methods Programs Biomed; 2018 Sep; 163():143-153. PubMed ID: 30119849
[TBL] [Abstract][Full Text] [Related]
29. Microscopic melanoma detection and classification: A framework of pixel-based fusion and multilevel features reduction.
Rehman A; Khan MA; Mehmood Z; Saba T; Sardaraz M; Rashid M
Microsc Res Tech; 2020 Apr; 83(4):410-423. PubMed ID: 31898863
[TBL] [Abstract][Full Text] [Related]
30. Deep Learning-Based Methods for Automatic Diagnosis of Skin Lesions.
El-Khatib H; Popescu D; Ichim L
Sensors (Basel); 2020 Mar; 20(6):. PubMed ID: 32245258
[TBL] [Abstract][Full Text] [Related]
31. Superior skin cancer classification by the combination of human and artificial intelligence.
Hekler A; Utikal JS; Enk AH; Hauschild A; Weichenthal M; Maron RC; Berking C; Haferkamp S; Klode J; Schadendorf D; Schilling B; Holland-Letz T; Izar B; von Kalle C; Fröhling S; Brinker TJ;
Eur J Cancer; 2019 Oct; 120():114-121. PubMed ID: 31518967
[TBL] [Abstract][Full Text] [Related]
32. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.
Alsaade FW; Aldhyani THH; Al-Adhaileh MH
Comput Math Methods Med; 2021; 2021():9998379. PubMed ID: 34055044
[TBL] [Abstract][Full Text] [Related]
33. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images.
Juntu J; Sijbers J; De Backer S; Rajan J; Van Dyck D
J Magn Reson Imaging; 2010 Mar; 31(3):680-9. PubMed ID: 20187212
[TBL] [Abstract][Full Text] [Related]
34. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.
Premaladha J; Ravichandran KS
J Med Syst; 2016 Apr; 40(4):96. PubMed ID: 26872778
[TBL] [Abstract][Full Text] [Related]
35. Hyperspectral imaging in automated digital dermoscopy screening for melanoma.
Hosking AM; Coakley BJ; Chang D; Talebi-Liasi F; Lish S; Lee SW; Zong AM; Moore I; Browning J; Jacques SL; Krueger JG; Kelly KM; Linden KG; Gareau DS
Lasers Surg Med; 2019 Mar; 51(3):214-222. PubMed ID: 30653684
[TBL] [Abstract][Full Text] [Related]
36. Comparing artificial intelligence algorithms to 157 German dermatologists: the melanoma classification benchmark.
Brinker TJ; Hekler A; Hauschild A; Berking C; Schilling B; Enk AH; Haferkamp S; Karoglan A; von Kalle C; Weichenthal M; Sattler E; Schadendorf D; Gaiser MR; Klode J; Utikal JS
Eur J Cancer; 2019 Apr; 111():30-37. PubMed ID: 30802784
[TBL] [Abstract][Full Text] [Related]
37. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems.
Acharya UR; Vinitha Sree S; Krishnan MM; Molinari F; Garberoglio R; Suri JS
Ultrasonics; 2012 Apr; 52(4):508-20. PubMed ID: 22154208
[TBL] [Abstract][Full Text] [Related]
38. Machine learning-based diagnosis of melanoma using macro images.
Gautam D; Ahmed M; Meena YK; Ul Haq A
Int J Numer Method Biomed Eng; 2018 May; 34(5):e2953. PubMed ID: 29266819
[TBL] [Abstract][Full Text] [Related]
39. Differentiation of melanoma from benign mimics using the relative-color method.
LeAnder R; Chindam P; Das M; Umbaugh SE
Skin Res Technol; 2010 Aug; 16(3):297-304. PubMed ID: 20636998
[TBL] [Abstract][Full Text] [Related]
40. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
Al-Masni MA; Al-Antari MA; Choi MT; Han SM; Kim TS
Comput Methods Programs Biomed; 2018 Aug; 162():221-231. PubMed ID: 29903489
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
