Name: CARLOS FREDERICO SILVA DA FONSECA MENDES
Publication date: 07/10/2022
Advisor:
Name |
Role |
|---|---|
| RENATO ANTÔNIO KROHLING | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| DANIEL CRUZ CAVALIÉRI | External Examiner * |
| RENATO ANTÔNIO KROHLING | Advisor * |
| VINICIUS FERNANDES SOARES MOTA | Internal Examiner * |
Summary: Skin lesions diagnostic is a challenging problem due to the variety of visual aspects of
the lesions. Since dermatologists make use of visual cues, lesion data and pacient data
(denominated here by clinical metadata), we investigate if the combination of features
from convolutional neural networks (CNN), handcrafted features and clinical metadata
can improve the performance of automated diagnoses of skin cancer. Most works on skin
lesion diagnosis in the literature use dermoscopic images without clinical metadata. In
order to address this problem, we used a clinical image dataset of skin lesion with patient
information collected via smartphone named PAD-UFES-20. With the proposed fusion
architecture we show that the results using clinical features as a complement to the CNN
and handcrafted features improve the classification in terms of balanced accuracy by 7.1%
for cancer and by 3.2% for melanoma as compared with only features extracted from a
CNN. In addition, our findings show that combining only handcrafted features with deep
features did not improve the results indicating the importance of using clinical metadata
for skin lesion classification.
