Sistema inteligente basado en redes neuronales para la identificación de cáncer de piel de tipo melanoma en imágenes de lesiones cutáneas

El cáncer de piel es uno de los tipos de cáncer más predominante en el mundo. Existen dos tipos principales de cáncer de piel: melanoma y no melanoma. Siendo el primero, el más agresivo y mortal. Al igual que con otros tipos de cánceres, la detección temprana y precisa de esta enfermedad en una persona, puede hacer que el tratamiento sea más eficaz y por consiguiente mejorar su calidad de vida. En el presente artículo, plantea como objetivo determinar de forma precisa si una imagen lesión cutánea representa un caso de cáncer de piel de tipo Melanoma, para ello se realizará el desarrollo de un sistema inteligente basado en un método de Aprendizaje Profundo usando Redes Neuronales. Los modelos de redes neuronales, fueron entrenados, validados y evaluados con el conjunto de datos de la competición SIIM-ISIC (SocietyforImagingInformatics in Medicine - International Skin ImagingCollaboration) del año 2020. Como resultado, se logró implementar un sistema inteligente ensamblando un módulo de clasificación de imágenes y un módulo de clasificación de metadata, obteniendo una probabilidad de desempeño de 92.85% Precisión, 71.50% Sensibilidad, 94.89% Especificidad

Convolutional Neural Networks on Assembling Classification Models to Detect Melanoma Skin Cancer

In 2020, there were more than 1.2 million new skin cancer diagnoses, and melanoma was the most recurrent type of cancer. On the other hand, melanoma is the least common but most serious form of skin cancer affecting both men and women. This work aims to assemble classification models to detect a case of melanoma with high accuracy based on a Convolutional Neural Networks system. The methodology considers training 21 models for image classification, with the best assembly performance of  EfficientNet and VGG-19 architectures,  the data augmentation technique was used to the images to improve its performance. The results show 92.85% of accuracy, 71.50% of sensitivity, and 94.89% of specificity, with an improvement of 0.06% in accuracy and specificity. The assembly of the classification models achieved higher accuracy in melanoma skin cancer image classification

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (>59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30\u201350 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of 6435 or a UHDRS motor score of 645 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P <.001). Overall motor and cognitive performance (P <.001) were worse, however only disease motor progression was slower (coefficient, 120.58; SE 0.16; P <.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P <.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P <.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (>59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30–50 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of ≤35 or a UHDRS motor score of ≤5 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P <.001). Overall motor and cognitive performance (P <.001) were worse, however only disease motor progression was slower (coefficient, −0.58; SE 0.16; P <.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P <.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P <.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients