| dc.description.abstract | As a topic of global health importance, skin diseases must be quickly identified and
accurately diagnosed to allow for effective treatment. Specifically for the classification
of skin diseases, the use of deep learning models in the analysis of medical images
has shown remarkable promise. To improve accuracy and predictability when classifying
skin diseases, this paper suggests using an ensemble model composed of the
ResNet-50, EfficientNet, Inception V3, MobileNet, NASNetMobile, DenseNet201
and Xception architectures. The first step of the investigation is to examine the
existing research on deep learning models used for skin disease diagnosis and categorisation.
ResNet-50, EfficientNet, MobileNet, Inception V3, DenseNet201, NASNetMobile
and Xception have demonstrated their efficacy in several medical imaging
applications, such as the identification and categorization of skin diseases. The utilization
of diagnostic and classification methods in the context of skin illnesses serves
as illustrative instances of such applications. It is important to note, however, that
every construction possesses inherent imperfections. The present study is further enhanced
by the use of a novel notion referred to as a ”ensemble,” which amalgamates
the most advantageous attributes of many models. To ensure proper functioning, the
ensemble model must initially extract and subsequently aggregate information. The
comprehensive set of fundamental models underwent training utilizing a vast dataset
of dermatological information. The objective of this training session was to acquire
the knowledge and skills necessary to identify and discern the distinguishing features
of skin lesions via the analysis of photographic representations. The ensemble model
incorporates feature-level fusion to aggregate information obtained from many base
models. When many data types are merged in this manner, it results in the creation
of a cohesive representation. In order to improve the process of classification and
generalization, the model utilizes the varied members of the ensemble. The efficacy
of the ensemble model is assessed by a wide array of experiments. These research
utilize a meticulously collected and standardized dataset encompassing many skinrelated
disorders. The ensemble model demonstrates superiority over the individual
models in terms of accuracy, precision, recall, and F1-score. The fusion methodology,
which leverages several sources, holds the potential to extract supplementary
data from diverse systems. The utilization of gradient rendering techniques enables
the comprehensive evaluation of a model’s readability. This study examines
the decision-making process of an ensemble in determining the salient features of a
picture for the purpose of labeling. This thesis presents an ensemble architecture
for identifying skin issues by utilizing ResNet-50, EfficientNet, Inception V3, MobileNet,
NASNetMobile, DenseNet201, and Xception models. When compared to
the gold standard dataset, the proposed model demonstrates superior performance,
indicating its potential to assist dermatologists in making more accurate diagnoses
in real-world clinical scenarios. | en_US |
