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https://doi.org/10.21256/zhaw-29936| Publikationstyp: | Thesis: Bachelor |
| Titel: | Evaluating XAI algorithms in skin cancer classification : a path towards trustworthy AI systems |
| Autor/-in: | Homa, Celina |
| Betreuer/-in / Gutachter/-in: | Reif, Monika Ulrike Hochberg, Alan |
| DOI: | 10.21256/zhaw-29936 |
| Umfang: | 78 |
| Erscheinungsdatum: | 2023 |
| Reihe: | Bachelorarbeiten ZHAW School of Engineering |
| Verlag / Hrsg. Institution: | ZHAW Zürcher Hochschule für Angewandte Wissenschaften |
| Verlag / Hrsg. Institution: | Winterthur |
| Sprache: | Englisch |
| Schlagwörter: | Machine learning; Artificial intelligence; Explainable AI; AIX360; Skin cancer detection; Image classification |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Artificial intelligence (AI) has become a valuable tool in skin cancer classification. However, its widespread adoption is limited by concerns about trustworthiness and its black-box nature. One promising approach to increasing trust is to improve the explainability of AI systems. This thesis explores the potential of using algorithms from the AIX360 library to achieve this goal. The algorithms - ProtoDash, DIP-VAE, LIME, SHAP and CEM - were evaluated for their runtime, simplicity, explainability/interpretability and stability in the context of skin cancer classification. The results suggest that each algorithm offers unique insights, but none can be considered universally superior. In particular, LIME and SHAP showed a promising balance between interpretability and stability, making them strong candidates. Further work is needed to identify the optimal combination of algorithms to increase confidence and to adapt these algorithms to handle complex data sets. This work is a step towards developing AI models that are not only effective, but also transparent and accountable, furthering the quest for trustworthy AI in healthcare. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/29936 |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Enthalten in den Sammlungen: | Bachelorarbeiten ZHAW School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2023_Homa_Celina_BA_SoE.pdf | 5.26 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Homa, C. (2023). Evaluating XAI algorithms in skin cancer classification : a path towards trustworthy AI systems [Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften]. https://doi.org/10.21256/zhaw-29936
Homa, C. (2023) Evaluating XAI algorithms in skin cancer classification : a path towards trustworthy AI systems. Bachelor’s thesis. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-29936.
C. Homa, “Evaluating XAI algorithms in skin cancer classification : a path towards trustworthy AI systems,” Bachelor’s thesis, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, Winterthur, 2023. doi: 10.21256/zhaw-29936.
HOMA, Celina, 2023. Evaluating XAI algorithms in skin cancer classification : a path towards trustworthy AI systems. Bachelor’s thesis. Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Homa, Celina. 2023. “Evaluating XAI Algorithms in Skin Cancer Classification : A Path towards Trustworthy AI Systems.” Bachelor’s thesis, Winterthur: ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-29936.
Homa, Celina. Evaluating XAI Algorithms in Skin Cancer Classification : A Path towards Trustworthy AI Systems. ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2023, https://doi.org/10.21256/zhaw-29936.
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