Authors :
Bhagyanie Chathurika; Sanvitha Kasthuriarachchi; Tharmika R.; Jayawardane W.M.A.V.; Udumullaarachchi O.S.; Fernando D.E.
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4vjw8s6b
DOI :
https://doi.org/10.5281/zenodo.8365765
Abstract :
Skin diseases pose significant challenges in the
field of dermatology. In recent years, Convolutional
Neural Networks (CNNs) have emerged as a powerful
tool for image recognition and analysis tasks. This
research paper presents a comprehensive study on the
application of CNNs for skin disease diagnosis.
We propose a CNN-based framework for skin
disease diagnosis, which utilizes a large dataset of
dermatological images to accurately identify various skin
diseases. The proposed model leverages the deep
learning capabilities of CNNs to learn discriminative
features from input images, enabling accurate and
efficient diagnosis. We demonstrate improved accuracy
and efficiency in skin disease diagnosis by employing
pre-trained models. Our proposed model enables
accurate classification of skin diseases into high,
medium, and low severity categories by leveraging a
large dataset of annotated images, assisting healthcare
professionals in prioritizing treatment strategies.
In conclusion, this research paper presents a
comprehensive study on the application of CNNs for skin
disease diagnosis, skin lesion classification, melanoma
skin cancer classification, and skin disease severity
classification. The proposed models showcase significant
advancements in the field of dermatology, providing
accurate and efficient tools for dermatologists and
healthcare professionals.
The findings of this research contribute to
improving the diagnosis, classification, and severity
assessment of skin diseases, ultimately enhancing patient
care and treatment outcomes.
Skin diseases pose significant challenges in the
field of dermatology. In recent years, Convolutional
Neural Networks (CNNs) have emerged as a powerful
tool for image recognition and analysis tasks. This
research paper presents a comprehensive study on the
application of CNNs for skin disease diagnosis.
We propose a CNN-based framework for skin
disease diagnosis, which utilizes a large dataset of
dermatological images to accurately identify various skin
diseases. The proposed model leverages the deep
learning capabilities of CNNs to learn discriminative
features from input images, enabling accurate and
efficient diagnosis. We demonstrate improved accuracy
and efficiency in skin disease diagnosis by employing
pre-trained models. Our proposed model enables
accurate classification of skin diseases into high,
medium, and low severity categories by leveraging a
large dataset of annotated images, assisting healthcare
professionals in prioritizing treatment strategies.
In conclusion, this research paper presents a
comprehensive study on the application of CNNs for skin
disease diagnosis, skin lesion classification, melanoma
skin cancer classification, and skin disease severity
classification. The proposed models showcase significant
advancements in the field of dermatology, providing
accurate and efficient tools for dermatologists and
healthcare professionals.
The findings of this research contribute to
improving the diagnosis, classification, and severity
assessment of skin diseases, ultimately enhancing patient
care and treatment outcomes.
