Cutaneous melanoma is considered the skin cancer with highest mortality rate
and has been gaining the attention of the medical community due to its rapidly
increasing incidence. Advancements in computational technologies have paved the
way for innovative image detection methods that can be transferable to medical ap plications, significantly enhancing the potential for early intervention in melanoma
diagnosis. To make diagnosis more accurate and to further increase survival rates,
this study employs deep learning techniques on an extensive dataset derived from
multiple sources. Utilizing Microsoft Azure Cloud as the computational infras tructure, trial and error approach was employed by hyperparameterizing several
convolutional neural networks (CNN) where the decision criteria were choosing the
one with highest Fβ Score. MAR-MELA-CNN is an innovative ensemble model in corporating six state-of-the-art pre-trained CNN architectures: Xception, VGG16,
ResNet50, NASNetMobile, MobileNetV2, and InceptionV3. The primary goal of
this research is to further understand CNN’s efficiency in the diagnosis of melanoma
and to furthermore measure its performance on a merged dataset. The proposed
algorithm achieved a Fβ score of 85%, an area under the curve (AUC) score of
93%, and an average precision (AP) score of 92%, promising diagnostic tool for
cutaneous melanoma compared to traditional methods. Further improvements lay
in the improvement of the architecture, expansion of the computational instances
as well as of the dataset. Another field of future work could be devising a strategy
for real-time implementation of this model in a hospital setting, as it could be of
vital importance to provide swift support to doctors.O melanoma cutˆaneo ´e considerado o cancro de pele com a maior taxa de mortal idade e tem vindo a ganhar a aten¸c˜ao da comunidade m´edica devido ao seu r´apido
aumento de incidˆencia. Os avan¸cos tecnol´ogicos contribu´ıram para m´etodos ino vadores de detec¸c˜ao de imagens transfer´ıveis para aplica¸c˜oes m´edicas, aumentando
significativamente o potencial de interven¸c˜ao precoce no diagn´ostico de melanoma.
Para tornar o diagn´ostico mais preciso e aumentar a taxa de sobrevivˆencia, este es tudo emprega t´ecnicas de aprendizagem profunda num conjunto alargado de dados
provenientes de v´arias fontes. Utilizando a infraestrutura computacional Microsoft
Azure Cloud, a abordagem de tentativa e erro foi utilizada ao hiperparametrizar
v´arias redes neuronais convolucionais, sendo o crit´erio de decis˜ao a escolha daquela
com a maior pontua¸c˜ao Fβ. MAR-MELA-CNN ´e um modelo ensemble que incor pora seis arquiteturas pr´e-treinadas: Xception, VGG16, ResNet50, NASNetMobile,
MobileNetV2 e InceptionV3. O objetivo principal desta investiga¸c˜ao ´e potenciar a
eficiˆencia das CNNs no diagn´ostico de melanoma e medir o seu desempenho num
conjunto de dados unificado. O algoritmo proposto alcan¸cou uma pontua¸c˜ao Fβ
de 85%, AUC de 93% e uma precis˜ao m´edia de 92%, tornando-se uma ferramenta
promissora para o diagn´ostico de melanoma em compara¸c˜ao com os m´etodos tradi cionais. Os desenvolvimentos futuros incluem a melhoria da arquitetura e a extens˜ao
das ferramentas computacionais e do conjunto de dados. Outro campo de trabalho
futuro poderia ser a cria¸c˜ao de uma estrat´egia de implementa¸c˜ao em tempo real
deste modelo num hospital, j´a que pode ser de vital importˆancia para fornecer
apoio imediato aos m´edicos
