Publications by authors named "G Mezquita"

Article Synopsis
  • Deep learning struggles with detecting and diagnosing medical image anomalies due to data imbalance, variability, and complexity, particularly in skin diseases that show significant differences in appearance and texture.
  • A new hybrid architecture combining wavelet decomposition with EfficientNet models has been developed to address these challenges, utilizing advanced techniques for data augmentation, loss functions, and optimization.
  • The proposed model demonstrated impressive accuracy rates of 94.7% and 92.2% when tested on the HAM10000 and ISIC2017 datasets, respectively.
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Background: The impact of Covid-19 on endometriosis patients is under-researched. Endometriosis has significant psychosocial effects on patients. Moreover, the mainstay of diagnosis and treatment of endometriosis is elective surgery, impacted as a result of healthcare strain.

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Objective: The aim of the study is to assess the safety and efficacy of transperitoneal laparoscopic para-aortic and pelvic lymphadenectomy in the setting of surgical staging of patients with locally advanced cervical cancer (LACC), and to analyse the prognostic value and impact of this staging on the survival prognosis of this condition.

Study Design: Data from 67 patients with LACC who underwent transperitoneal laparoscopic para-aortic and pelvic lymphadenectomy and who received chemo-radiation therapy were retrospectively analysed.

Results: Metastatic lymph nodes (LN) were identified in 32 patients (47.

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The information available on the safety of medicines in pregnant women is limited and must be suitably channeled. Medication that is applied topically or as eye drops can present relevant systemic absorption and cross the placenta barrier or enter maternal milk. We have reviewed ophthalmic medicines according to the fetal risk categories proposed by the classification of the Food and Drug Administration (FDA).

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