Automated Diagnosis of Skin Cancer.
Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient's clinical info...
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| Format: | Thesis |
| Language: | English |
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Kabale University
2025
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| Online Access: | http://hdl.handle.net/20.500.12493/2777 |
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| _version_ | 1823844184280268800 |
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| author | Nyiramugisha, Shallon Nuwahereza, Godfrey |
| author_facet | Nyiramugisha, Shallon Nuwahereza, Godfrey |
| author_sort | Nyiramugisha, Shallon |
| collection | KAB-DR |
| description | Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient's clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep-learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models’ performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models’ performance is significant and shows the importance of including these features in automated skin cancer detection. |
| format | Thesis |
| id | oai:idr.kab.ac.ug:20.500.12493-2777 |
| institution | KAB-DR |
| language | English |
| publishDate | 2025 |
| publisher | Kabale University |
| record_format | dspace |
| spelling | oai:idr.kab.ac.ug:20.500.12493-27772025-01-16T00:00:51Z Automated Diagnosis of Skin Cancer. Nyiramugisha, Shallon Nuwahereza, Godfrey Automated Diagnosis Skin Cancer Skin cancer is one of the most common types of cancer worldwide. Over the past few years, different approaches have been proposed to deal with automated skin cancer detection. Nonetheless, most of them are based only on dermoscopic images and do not take into account the patient's clinical information, an important clue towards clinical diagnosis. In this work, we present an approach to fill this gap. First, we introduce a new dataset composed of clinical images, collected using smartphones, and clinical data related to the patient. Next, we propose a straightforward method that includes an aggregation mechanism in well-known deep-learning models to combine features from images and clinical data. Last, we carry out experiments to compare the models’ performance with and without using this mechanism. The results present an improvement of approximately 7% in balanced accuracy when the aggregation method is applied. Overall, the impact of clinical data on models’ performance is significant and shows the importance of including these features in automated skin cancer detection. 2025-01-15T08:39:38Z 2025-01-15T08:39:38Z 2024 Thesis Nyiramugisha, S. & Nuwahereza, G. (2024). Automated Diagnosis of Skin Cancer. Kabale: Kabale University. http://hdl.handle.net/20.500.12493/2777 en Attribution-NonCommercial-NoDerivs 3.0 United States http://creativecommons.org/licenses/by-nc-nd/3.0/us/ application/pdf Kabale University |
| spellingShingle | Automated Diagnosis Skin Cancer Nyiramugisha, Shallon Nuwahereza, Godfrey Automated Diagnosis of Skin Cancer. |
| title | Automated Diagnosis of Skin Cancer. |
| title_full | Automated Diagnosis of Skin Cancer. |
| title_fullStr | Automated Diagnosis of Skin Cancer. |
| title_full_unstemmed | Automated Diagnosis of Skin Cancer. |
| title_short | Automated Diagnosis of Skin Cancer. |
| title_sort | automated diagnosis of skin cancer |
| topic | Automated Diagnosis Skin Cancer |
| url | http://hdl.handle.net/20.500.12493/2777 |
| work_keys_str_mv | AT nyiramugishashallon automateddiagnosisofskincancer AT nuwaherezagodfrey automateddiagnosisofskincancer |