## 3 Materials and Methods
### Databases  
The ORIGA (Online Retinal Fundus Image Dataset for Glaucoma Analysis and Research) is an accessible database containing 168 color fundus images illustrating the posterior pole with glaucoma and 482 images without any pathologies. These images were acquired as part of the Singapore Malay Eye Study, a screening program conducted by the Singapore Eye Research Institute [26].  
Messidor, designed for diabetic retinopathy, encompasses 1200 retina images labeled by experts, both with and without glaucoma, with 800 of them acquired using dilated pupils. This collection was obtained during a screening program in Switzerland, focusing on diabetic patients aged 25-90, funded by the French Ministry under the Techno-Vison 2004 program [27].  
EyePacs is a database available on Kaggle, containing 35,126 images of diabetic retinopathy obtained through screening programs [28].  
The HRF (High-Resolution Fundus Image Database), provided by the CS5 Laboratory, consists of 15 normal fundus images, 15 images with glaucoma, and 15 images with diabetic retinopathy [29].  
ISIC (International Skin Imaging Collaboration) includes 93,083 images of skin lesions for testing algorithms in melanoma detection, serving as the largest public database of dermatoscopic images. The database encourages the development of effective systems for detecting skin lesions and incorporates smaller databases like HAM 1000, BCN2000, and MSK [30].  
A Kaggle-hosted database features 601 images showcasing various pathologies, including cataract, glaucoma, and diabetic retinopathy, divided into five classes [31].  
RIM-ONE, a database of retinal images from ocular fundus images, comprises 200 images with glaucoma and 255 images without pathologies [32; 33].  
DermIS, the most comprehensive database, includes dermatological images representing nearly all types of skin conditions, acquired in collaboration with the University of Heidelberg and the Department of Dermatology at the University of Erlangen. It contains 500 images depicting melanoma and 500 non-melanoma images [34].  
MED-NODE consists of 170 images, with 70 showcasing melanoma and 100 displaying normal pathology, acquired by the Department of Dermatology at the University Medical Center Groningen for training and testing the MED-NODE system for detecting cancer from macroscopic images [35].  
PH\({}^{2}\) encompasses 40 dermatoscopic images representing melanoma and 80 images without pathologies. Developed for testing algorithms in scientific research projects, this database was acquired at the dermatology department of Pedro Hispano Hospital in Matosinhos, Portugal, using the same method and having a resolution of 768 \(\times\) 560 pixels [36; 37; 38; 39].  
\begin{table}
\begin{tabular}{c c} \hline
**Class** & **Image Number** \\ \hline \multicolumn{2}{c}{**Ocular pathologies**} \\ Glaucoma & 1150 \\ Cataract & 1176 \\ Diabetic retinopathy & 1006 \\ Normal & 1299 \\ \multicolumn{2}{c}{**Skin pathologies**} \\ Melanoma & 2463 \\ Normal & 2463 \\ Actinic keratosis & 2166 \\ Seborrheic keratosis & 2764 \\ Basal cell carcinoma & 2764 \\ \hline \end{tabular}
\end{table}
Table 1: Classes and the number of images based on pathology.