@Article{cmes.2020.010943,
AUTHOR = {Inzamam Mashood Nasir, Muhammad Attique Khan, Majed Alhaisoni, Tanzila Saba, Amjad Rehman, Tassawar Iqbal},
TITLE = {A Hybrid Deep Learning Architecture for the Classification of Superhero Fashion Products: An Application for Medical-Tech Classification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {3},
PAGES = {1017--1033},
URL = {http://www.techscience.com/CMES/v124n3/39925},
ISSN = {1526-1506},
ABSTRACT = {Comic character detection is becoming an exciting and growing
research area in the domain of machine learning. In this regard, recently, many
methods are proposed to provide adequate performance. However, most of these
methods utilized the custom datasets, containing a few hundred images and fewer
classes, to evaluate the performances of their models without comparing it, with
some standard datasets. This article takes advantage of utilizing a standard publicly dataset taken from a competition, and proposes a generic data balancing
technique for imbalanced dataset to enhance and enable the in-depth training of
the CNN. In addition, to classify the superheroes efficiently, a custom 17-layer
deep convolutional neural network is also proposed. The computed results
achieved overall classification accuracy of 97.9% which is significantly superior
to the accuracy of competition’s winner.},
DOI = {10.32604/cmes.2020.010943}
}