<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping health-care in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROCAUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explain ability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.</p>
</abstract>

Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping health-care in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROCAUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explain ability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.

Skin Lesion Detection Using Deep Learning

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Skin Lesion Detection Using Deep Learning"}

Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping...

Skin Lesion Detection Using Deep Learning

Data publikacji: 06 wrz 2023

Zakres stron: 56 - 64

Otrzymano: 12 maj 2022

Przyjęty: 28 lip 2022

DOI: https://doi.org/10.14313/jamris/3-2022/24

Słowa kluczowe
Skin lesion, DenseNet, Inception V3

© 2022 Rajit Chandra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Skin Lesion Detection Using Deep Learning

Data publikacji: 06 wrz 2023

Zakres stron: 56 - 64

Otrzymano: 12 maj 2022

Przyjęty: 28 lip 2022

DOI: https://doi.org/10.14313/jamris/3-2022/24

Słowa kluczoweSkin lesion, DenseNet, Inception V3

© 2022 Rajit Chandra et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Skin lesion, DenseNet, Inception V3