Deep Learning-Based Classification of Melanoma and Non-Melanoma Skin Cancer Article Swipe
Eatedal Alabdulkreem
,
Hela Elmannai
,
Aymen Saad
,
Israa S. Kamil
,
Ahmed Elaraby
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18280/ts.410117
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.18280/ts.410117
Melanoma skin cancer is primarily characterized by poor prognostic responses.Surgical treatment can achieve advanced cure rate with early melanoma detection.Manual segmentation of suspected lesions aids early melanoma diagnosis.However, the limitations of manual segmentation include low efficiency and a risk of misclassification.Deep learning, due to its proficiency in image object classification, has gained popularity and is usually used in medical specialties such as ophthalmology, dermatology, and radiology.This paper proposes a deep learning method using a novel light weight convolutional neural networks (LWCNN) and transfer learning techniques (GoogleNet,.These are used to train datasets and features enhancement of skin scan gathered from Kaggle, aiming to distinguish them into two groups: Melanoma and Non-Melanoma cells.By employing these techniques, new datasets with robust features are produced.All CNN models have been tested in two experiments.In firestone, model was tested solely with original datasets and achieved 97.30%, 88.43%, and 48.28% for AC-Training, AC-Testing, and Time (min) respectively.In second experiment, we used the dataset after enhancing the features of skin scan images, which resulted in 99.18%, 91.05%, and 22.54% for AC-Training, AC-Testing, and Time (min) respectively.According to experimental results, the proposed approach provides higher accuracy results for enhanced images than original images, demonstrating its potential in skin cancer classification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/ts.410117
- https://iieta.org/download/file/fid/122757
- OA Status
- hybrid
- Cited By
- 7
- References
- 43
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- 10
- OpenAlex ID
- https://openalex.org/W4392349136
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4392349136Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18280/ts.410117Digital Object Identifier
- Title
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Deep Learning-Based Classification of Melanoma and Non-Melanoma Skin CancerWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-29Full publication date if available
- Authors
-
Eatedal Alabdulkreem, Hela Elmannai, Aymen Saad, Israa S. Kamil, Ahmed ElarabyList of authors in order
- Landing page
-
https://doi.org/10.18280/ts.410117Publisher landing page
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https://iieta.org/download/file/fid/122757Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://iieta.org/download/file/fid/122757Direct OA link when available
- Concepts
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Melanoma, Skin cancer, Medicine, Dermatology, Artificial intelligence, Cancer, Cancer research, Computer science, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 4, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.in | 46, 57, 126, 166, 197 |
| abstract_inverted_index.is | 3, 54 |
| abstract_inverted_index.of | 21, 30, 39, 94, 160 |
| abstract_inverted_index.to | 43, 88, 101, 178 |
| abstract_inverted_index.we | 152 |
| abstract_inverted_index.CNN | 121 |
| abstract_inverted_index.and | 36, 53, 64, 81, 91, 108, 137, 141, 146, 169, 174 |
| abstract_inverted_index.are | 86, 119 |
| abstract_inverted_index.can | 11 |
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| abstract_inverted_index.for | 143, 171, 188 |
| abstract_inverted_index.has | 50 |
| abstract_inverted_index.its | 44, 195 |
| abstract_inverted_index.low | 34 |
| abstract_inverted_index.new | 114 |
| abstract_inverted_index.the | 28, 154, 158, 181 |
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| abstract_inverted_index.was | 131 |
| abstract_inverted_index.Time | 147, 175 |
| abstract_inverted_index.aids | 24 |
| abstract_inverted_index.been | 124 |
| abstract_inverted_index.cure | 14 |
| abstract_inverted_index.deep | 69 |
| abstract_inverted_index.from | 98 |
| abstract_inverted_index.have | 123 |
| abstract_inverted_index.into | 104 |
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| abstract_inverted_index.rate | 15 |
| abstract_inverted_index.risk | 38 |
| abstract_inverted_index.scan | 96, 162 |
| abstract_inverted_index.skin | 1, 95, 161, 198 |
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| abstract_inverted_index.than | 191 |
| abstract_inverted_index.them | 103 |
| abstract_inverted_index.used | 56, 87, 153 |
| abstract_inverted_index.with | 16, 116, 134 |
| abstract_inverted_index.(min) | 148, 176 |
| abstract_inverted_index.after | 156 |
| abstract_inverted_index.early | 17, 25 |
| abstract_inverted_index.image | 47 |
| abstract_inverted_index.light | 75 |
| abstract_inverted_index.model | 130 |
| abstract_inverted_index.novel | 74 |
| abstract_inverted_index.paper | 66 |
| abstract_inverted_index.these | 112 |
| abstract_inverted_index.train | 89 |
| abstract_inverted_index.using | 72 |
| abstract_inverted_index.which | 164 |
| abstract_inverted_index.22.54% | 170 |
| abstract_inverted_index.48.28% | 142 |
| abstract_inverted_index.aiming | 100 |
| abstract_inverted_index.cancer | 2, 199 |
| abstract_inverted_index.gained | 51 |
| abstract_inverted_index.higher | 185 |
| abstract_inverted_index.images | 190 |
| abstract_inverted_index.manual | 31 |
| abstract_inverted_index.method | 71 |
| abstract_inverted_index.models | 122 |
| abstract_inverted_index.neural | 78 |
| abstract_inverted_index.object | 48 |
| abstract_inverted_index.robust | 117 |
| abstract_inverted_index.second | 150 |
| abstract_inverted_index.solely | 133 |
| abstract_inverted_index.tested | 125, 132 |
| abstract_inverted_index.weight | 76 |
| abstract_inverted_index.(LWCNN) | 80 |
| abstract_inverted_index.88.43%, | 140 |
| abstract_inverted_index.91.05%, | 168 |
| abstract_inverted_index.97.30%, | 139 |
| abstract_inverted_index.99.18%, | 167 |
| abstract_inverted_index.Kaggle, | 99 |
| abstract_inverted_index.achieve | 12 |
| abstract_inverted_index.dataset | 155 |
| abstract_inverted_index.groups: | 106 |
| abstract_inverted_index.images, | 163, 193 |
| abstract_inverted_index.include | 33 |
| abstract_inverted_index.lesions | 23 |
| abstract_inverted_index.medical | 58 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.usually | 55 |
| abstract_inverted_index.Melanoma | 0, 107 |
| abstract_inverted_index.accuracy | 186 |
| abstract_inverted_index.achieved | 138 |
| abstract_inverted_index.advanced | 13 |
| abstract_inverted_index.approach | 183 |
| abstract_inverted_index.cells.By | 110 |
| abstract_inverted_index.datasets | 90, 115, 136 |
| abstract_inverted_index.enhanced | 189 |
| abstract_inverted_index.features | 92, 118, 159 |
| abstract_inverted_index.gathered | 97 |
| abstract_inverted_index.learning | 70, 83 |
| abstract_inverted_index.melanoma | 18, 26 |
| abstract_inverted_index.networks | 79 |
| abstract_inverted_index.original | 135, 192 |
| abstract_inverted_index.proposed | 182 |
| abstract_inverted_index.proposes | 67 |
| abstract_inverted_index.provides | 184 |
| abstract_inverted_index.resulted | 165 |
| abstract_inverted_index.results, | 180 |
| abstract_inverted_index.transfer | 82 |
| abstract_inverted_index.employing | 111 |
| abstract_inverted_index.enhancing | 157 |
| abstract_inverted_index.learning, | 41 |
| abstract_inverted_index.potential | 196 |
| abstract_inverted_index.primarily | 4 |
| abstract_inverted_index.suspected | 22 |
| abstract_inverted_index.treatment | 10 |
| abstract_inverted_index.efficiency | 35 |
| abstract_inverted_index.firestone, | 129 |
| abstract_inverted_index.popularity | 52 |
| abstract_inverted_index.prognostic | 8 |
| abstract_inverted_index.techniques | 84 |
| abstract_inverted_index.AC-Testing, | 145, 173 |
| abstract_inverted_index.distinguish | 102 |
| abstract_inverted_index.enhancement | 93 |
| abstract_inverted_index.experiment, | 151 |
| abstract_inverted_index.limitations | 29 |
| abstract_inverted_index.proficiency | 45 |
| abstract_inverted_index.specialties | 59 |
| abstract_inverted_index.techniques, | 113 |
| abstract_inverted_index.AC-Training, | 144, 172 |
| abstract_inverted_index.Non-Melanoma | 109 |
| abstract_inverted_index.dermatology, | 63 |
| abstract_inverted_index.experimental | 179 |
| abstract_inverted_index.produced.All | 120 |
| abstract_inverted_index.segmentation | 20, 32 |
| abstract_inverted_index.characterized | 5 |
| abstract_inverted_index.convolutional | 77 |
| abstract_inverted_index.demonstrating | 194 |
| abstract_inverted_index.experiments.In | 128 |
| abstract_inverted_index.ophthalmology, | 62 |
| abstract_inverted_index.radiology.This | 65 |
| abstract_inverted_index.classification, | 49 |
| abstract_inverted_index.classification. | 200 |
| abstract_inverted_index.respectively.In | 149 |
| abstract_inverted_index.detection.Manual | 19 |
| abstract_inverted_index.(GoogleNet,.These | 85 |
| abstract_inverted_index.diagnosis.However, | 27 |
| abstract_inverted_index.responses.Surgical | 9 |
| abstract_inverted_index.misclassification.Deep | 40 |
| abstract_inverted_index.respectively.According | 177 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5102737261 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I189575948, https://openalex.org/I4210092650 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.91195684 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |