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Optimal selection of input features and an acompanying neural network structure for the classification purposes - skin lesions case study
Abstract
Malignant melanomas are the most deadly type of skin cancers however detected early enough give a high chances for successful treatment. The last years saw the dynamic growth of interest of automatic computer-aided skin cancer diagnosis. Every month brings new research results on new approaches to this problem, new methods of preprocessing, new classifiers, new ideas to follow etc. In particular, the rapid development of dermatoscopy, image processing methods, as well as the ever-increasing computing power of computers caused that researchers are able to consider significantly more features of the analyzed lesion than has been done so far using methods recognized in a medical community such as ABCD or Menzies methods. From the other hand more features not always imply an improvement in terms of efficiency of the diagnosis and its transparency. Hence, in this paper we survey the kind of features taken into account by the researchers and then, selected the most efficient set of them. Proposed method jointly selects the optimal set of features representing the analyzed lesion together with the accompanying form of the neural classifier (number of neurons, activation functions). The evolutionary algorithms are used in order to carry out the optimization. Obtained results are even better than the ones obtained by the most efficient these days deep classifiers.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 23rd International Conference on Methods and Models in Automation and Robotics (MMAR)
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Grochowski M., Mikołajczyk A., Kwasigroch A.: Optimal selection of input features and an acompanying neural network structure for the classification purposes - skin lesions case study// 2018 23rd International Conference on Methods and Models in Automation and Robotics (MMAR)/ ed. 2018 : , 2018,
- Verified by:
- Gdańsk University of Technology
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