Authors :
Radhey Khandelwal
Volume/Issue :
Volume 8 - 2023, Issue 10 - October
Google Scholar :
https://tinyurl.com/ysrafpwa
Scribd :
https://tinyurl.com/bdhxcv5a
DOI :
https://doi.org/10.5281/zenodo.10078152
Abstract :
Dermatological diseases are highly prevalent
and affect individuals of all ages and genders. Accurate
prediction of these diseases is crucial for timely diagnosis
and effective treatment. Skin lesions, characterized by
variations in color, shape, and texture, serve as
important indicators of dermatological conditions. In
this research, we have conducted a comparative analysis
of different models to detect and recognize skin diseases.
The objective of our study is to develop a model that can
accurately predict various dermatological diseases.
The importance of our research lies in addressing
the widespread nature of dermatological diseases and the
need for reliable and efficient prediction methods. By
employing machine learning techniques, we aim to
provide a tool that can assist dermatologists in their
diagnosis and decision-making processes.
To determine the most effective approach, we
evaluated the performance of various models. Among
them, densenet121 demonstrated the highest accuracy
and reliability. We got an accuracy of 90.6% using this
model. Therefore, we selected densenet121 as the basis
for our proposed method.
By implementing the densenet121 model, we
achieved significant improvements in the prediction of
dermatological diseases. Our findings indicate that this
model can accurately identify and classify different skin
lesions, enabling early detection and timely intervention.
In conclusion, our research highlights the
significance of accurate prediction models in the field of
dermatology. The utilization of densenet121 as a basis
for our proposed method shows promising results,
emphasizing its potential as an efficient tool for
dermatological disease prediction. The development and
integration of such models into clinical practice can
significantly contribute to improved patient outcomes
and enhance the overall management of dermatological
conditions.
Keywords :
Skin Lesion, Neural Network,, Convolutional Neural Network, DenseNet-121
Dermatological diseases are highly prevalent
and affect individuals of all ages and genders. Accurate
prediction of these diseases is crucial for timely diagnosis
and effective treatment. Skin lesions, characterized by
variations in color, shape, and texture, serve as
important indicators of dermatological conditions. In
this research, we have conducted a comparative analysis
of different models to detect and recognize skin diseases.
The objective of our study is to develop a model that can
accurately predict various dermatological diseases.
The importance of our research lies in addressing
the widespread nature of dermatological diseases and the
need for reliable and efficient prediction methods. By
employing machine learning techniques, we aim to
provide a tool that can assist dermatologists in their
diagnosis and decision-making processes.
To determine the most effective approach, we
evaluated the performance of various models. Among
them, densenet121 demonstrated the highest accuracy
and reliability. We got an accuracy of 90.6% using this
model. Therefore, we selected densenet121 as the basis
for our proposed method.
By implementing the densenet121 model, we
achieved significant improvements in the prediction of
dermatological diseases. Our findings indicate that this
model can accurately identify and classify different skin
lesions, enabling early detection and timely intervention.
In conclusion, our research highlights the
significance of accurate prediction models in the field of
dermatology. The utilization of densenet121 as a basis
for our proposed method shows promising results,
emphasizing its potential as an efficient tool for
dermatological disease prediction. The development and
integration of such models into clinical practice can
significantly contribute to improved patient outcomes
and enhance the overall management of dermatological
conditions.
Keywords :
Skin Lesion, Neural Network,, Convolutional Neural Network, DenseNet-121
