Early diagnosis of skin cancer is critical to treatment and saving
patients’ lives, many studies have used Convolutional Neural Networks
(CNNs) to achieve this goal. Traditional methods using the Cross
Entropy (CE) loss function, however, often struggle with classes that
are easily confused, such as Nevus and Melanoma, leading to reduced
diagnostic accuracy. To address this, we propose the Confusion-aware
Cross Entropy (CCE) loss function, which enhances classification
performance by focusing on these easily confused classes. Our method
computes the mean of the negative class logits to identify these classes,
ensuring the loss calculation prioritizes their accurate classification.
Experiments conducted on the publicly available HAM10000 dataset
using ResNet50, EfficientNet-B4, Inception-V3, and DenseNet121
demonstrate that our approach significantly outperforms the
traditional CE loss function, achieving higher Accuracy, Sensitivity,
and Precision. These results underscore the potential of the CCE loss
function to improve clinical outcomes by providing more reliable skin
lesion classifications.
Qichen Su, Haza Nuzly Abdull Hamed
University Technology Malaysia, Malaysia
Skin Lesion Classification, Cross Entropy, Loss Function, Confusion Aware, CNNs
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 2 , Pages: 3407 - 3410 )
Date of Publication :
November 2024
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