TY - GEN
T1 - Generalized Skin Cancer Image Classification Performance Using Xception Model
AU - Memon, Qurban A.
AU - Al Ameri, Ghaya
AU - Musthafa, Namya
AU - AlShamsi, Aryam
AU - AlYaqoubi, Aisha
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Skin cancer is well known and regarded as one of the most common types of cancer, killing millions of people worldwide. Detecting and categorizing cancer at an early stage can be advantageous, leading to a faster and higher success rate of therapy. Intelligent technologies are currently being used to classify skin lesions. The fundamental goal of our experimental research is to investigate biomedical skin cancer datasets in order to develop an effective approach for determining whether a cancer is malignant or benign. To train and categorize the dataset images, CNN (sequential), ResNet-50, Inception v3, and Xception models are employed. Two large and balanced datasets are collected for this purpose. One is used to compare the performance of employed model algorithms. Next, the selected model is again retrained on the second dataset for validation and generalization purposes. It turns out that the performance of Xception model is generalized and outperforms other models in accuracy. Experimental results are tabulated and graphed using accuracy and confusion matrix.
AB - Skin cancer is well known and regarded as one of the most common types of cancer, killing millions of people worldwide. Detecting and categorizing cancer at an early stage can be advantageous, leading to a faster and higher success rate of therapy. Intelligent technologies are currently being used to classify skin lesions. The fundamental goal of our experimental research is to investigate biomedical skin cancer datasets in order to develop an effective approach for determining whether a cancer is malignant or benign. To train and categorize the dataset images, CNN (sequential), ResNet-50, Inception v3, and Xception models are employed. Two large and balanced datasets are collected for this purpose. One is used to compare the performance of employed model algorithms. Next, the selected model is again retrained on the second dataset for validation and generalization purposes. It turns out that the performance of Xception model is generalized and outperforms other models in accuracy. Experimental results are tabulated and graphed using accuracy and confusion matrix.
KW - Machine Learning
KW - Skin Cancer Classification
KW - Xception model
UR - https://www.scopus.com/pages/publications/105007132097
UR - https://www.scopus.com/pages/publications/105007132097#tab=citedBy
U2 - 10.1007/978-3-031-78946-5_34
DO - 10.1007/978-3-031-78946-5_34
M3 - Conference contribution
AN - SCOPUS:105007132097
SN - 9783031789458
T3 - Lecture Notes in Networks and Systems
SP - 355
EP - 365
BT - Bio-Inspired Computing - Proceedings of the 14th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2023
A2 - Sakalauskas, Virgilijus
A2 - Bajaj, Anu
A2 - Abraham, Ajith
A2 - Madhavi, K. Reddy
A2 - Manghirmalani Mishra, Pooja
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Innovations in Bio-Inspired Computing and Applications and 13th World Congress on Information and Communication Technologies, IBICA-WICT 2023
Y2 - 14 December 2023 through 15 December 2023
ER -
