Ms. Ankita Chatterjee Profile

Ankita Chatterjee

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Proceedings Article | 3 October 2022 Poster + Paper

Optimization of image recognition for the identification of psoriasis and eczema

Ankita Chatterjee

Proceedings Volume 12227, 122270U (2022) https://doi.org/10.1117/12.2632876

KEYWORDS: Artificial intelligence, Skin, Statistical modeling, Machine learning, Diagnostics, Data modeling, Visualization, Statistical analysis, Prototyping, Photodynamic therapy

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While they have similar symptoms, Psoriasis and Eczema have vastly different underlying causes and behaviors. Our research explores state of the art Deep Learning techniques (ResNet152 and MobileNetV2) for distinguishing Psoriasis and Eczema. The dataset includes 99 images of hand Psoriasis, 102 hand Eczema, 49 foot Psoriasis, and 42 foot Eczema. Using ResNet152 and MobileNetV2 in a TensorFlow prototype, and hyper-parameter tuning both across a range of parameters, we demonstrated that both architectures deliver substantial accuracy improvements over past research. MobileNetV2 foot samples had a predictive accuracy of 94.11%, ResNet152 foot samples had a highest accuracy of 100%. The two deep learning solutions also distinguished conditions for hand images with accuracies ranging from 73.68-89.47%. The results are optimistic for using deep learning with skin images for diagnostic assistance for these two conditions. For future work, we are exploring image featurization techniques in combination with both neural networks and statistical machine learning techniques to further improve predictive performance.

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