Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313289316> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4313289316 abstract "Skin diseases are one of the most common illnesses in human beings worldwide. The early detection of skin diseases becomes important venture to reduce diseases mortality, transmission and improvement. The commonly used methods for skin diseases prediction are Image Processing, Machine Learning, Deep Learning. Among them, now a day's deep learning methods are most common in skin disease prediction. The first step in skin disease prediction using deep learning is image preprocessing. This step plays an important role in diseases prediction because excellent image quality can increase the deep learning models generalization capability. Data cleaning and data conversion are two types of image preprocessing methods. Several methods for image preprocessing have already been proposed by researchers, and various works are also progressing in this field. Each technique has its very own advantages and disadvantages. This study reviews various image preprocessing techniques existing for the identification of skin diseases prediction using deep learning techniques." @default.
- W4313289316 created "2023-01-06" @default.
- W4313289316 creator A5007256164 @default.
- W4313289316 creator A5026542668 @default.
- W4313289316 date "2022-09-21" @default.
- W4313289316 modified "2023-09-27" @default.
- W4313289316 title "Image Preprocessing Techniques in Skin Diseases Prediction using Deep Learning: A Review" @default.
- W4313289316 cites W1982304064 @default.
- W4313289316 cites W2023191766 @default.
- W4313289316 cites W2097117768 @default.
- W4313289316 cites W2581082771 @default.
- W4313289316 cites W2592124696 @default.
- W4313289316 cites W2752782242 @default.
- W4313289316 cites W2803575519 @default.
- W4313289316 cites W2899425762 @default.
- W4313289316 cites W2914959431 @default.
- W4313289316 cites W2954996726 @default.
- W4313289316 cites W2956015785 @default.
- W4313289316 cites W2963059730 @default.
- W4313289316 doi "https://doi.org/10.1109/icirca54612.2022.9985547" @default.
- W4313289316 hasPublicationYear "2022" @default.
- W4313289316 type Work @default.
- W4313289316 citedByCount "0" @default.
- W4313289316 crossrefType "proceedings-article" @default.
- W4313289316 hasAuthorship W4313289316A5007256164 @default.
- W4313289316 hasAuthorship W4313289316A5026542668 @default.
- W4313289316 hasConcept C10551718 @default.
- W4313289316 hasConcept C108583219 @default.
- W4313289316 hasConcept C115961682 @default.
- W4313289316 hasConcept C119857082 @default.
- W4313289316 hasConcept C134306372 @default.
- W4313289316 hasConcept C153180895 @default.
- W4313289316 hasConcept C154945302 @default.
- W4313289316 hasConcept C177148314 @default.
- W4313289316 hasConcept C202444582 @default.
- W4313289316 hasConcept C33923547 @default.
- W4313289316 hasConcept C34736171 @default.
- W4313289316 hasConcept C41008148 @default.
- W4313289316 hasConcept C9417928 @default.
- W4313289316 hasConcept C9652623 @default.
- W4313289316 hasConceptScore W4313289316C10551718 @default.
- W4313289316 hasConceptScore W4313289316C108583219 @default.
- W4313289316 hasConceptScore W4313289316C115961682 @default.
- W4313289316 hasConceptScore W4313289316C119857082 @default.
- W4313289316 hasConceptScore W4313289316C134306372 @default.
- W4313289316 hasConceptScore W4313289316C153180895 @default.
- W4313289316 hasConceptScore W4313289316C154945302 @default.
- W4313289316 hasConceptScore W4313289316C177148314 @default.
- W4313289316 hasConceptScore W4313289316C202444582 @default.
- W4313289316 hasConceptScore W4313289316C33923547 @default.
- W4313289316 hasConceptScore W4313289316C34736171 @default.
- W4313289316 hasConceptScore W4313289316C41008148 @default.
- W4313289316 hasConceptScore W4313289316C9417928 @default.
- W4313289316 hasConceptScore W4313289316C9652623 @default.
- W4313289316 hasLocation W43132893161 @default.
- W4313289316 hasOpenAccess W4313289316 @default.
- W4313289316 hasPrimaryLocation W43132893161 @default.
- W4313289316 hasRelatedWork W2889453578 @default.
- W4313289316 hasRelatedWork W2949506716 @default.
- W4313289316 hasRelatedWork W3126844601 @default.
- W4313289316 hasRelatedWork W3215374478 @default.
- W4313289316 hasRelatedWork W4200250512 @default.
- W4313289316 hasRelatedWork W4211209597 @default.
- W4313289316 hasRelatedWork W4285479813 @default.
- W4313289316 hasRelatedWork W4312831135 @default.
- W4313289316 hasRelatedWork W4313289316 @default.
- W4313289316 hasRelatedWork W4316087074 @default.
- W4313289316 isParatext "false" @default.
- W4313289316 isRetracted "false" @default.
- W4313289316 workType "article" @default.