Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This project proposes an automated technique for skin cancer classification using images from the International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated significant advancements in artificial intelligence (AI) research. An automated system that recognizes and classifies skin cancer through deep learning techniques could prove useful in the medical field, as it can accurately detect the presence of skin cancer at an early stage. The ISIC dataset, which includes a vast collection of images of various skin conditions, provides an excellent opportunity to develop and validate deep learning algorithms for skin cancer classification. The proposed technique could have a significant impact on the medical industry by reducing the workload of medical personnel while providing accurate and timely diagnoses.

Detección de carcinoma basocelular utilizando red neuronal convolucional y Support Vector Machine

El cáncer de piel es uno de los tipos de cáncer más frecuente en los seres humanos, abarca cerca de un tercio total de las neoplasias. Dentro del cáncer de piel encontramos al carcinoma basocelular (CBC) siendo este el tipo de cáncer más frecuente a nivel mundial. Una serie de estudios que involucran enfoques de aprendizaje profundo ya se han desempeñado en un número considerable como la clasificación de imágenes. Los modelos utilizados en dichas tareas emplean la función Softmax (modelo clásico) en la capa de clasificación. Sin embargo, se han realizado estudios que utilizan una alternativa a la función Softmax para la clasificación: la máquina de vectores de soporte (SVM). El uso de SVM en una arquitectura de red neuronal artificial produce resultados relativamente mejores que el uso de la función Softmax convencional. Por este motivo se construyó un sistema que diagnostica el carcinoma basocelular implementando un modelo híbrido de red neuronal convolucional y máquina de vectores de soporte para clasificar el CBC. Los resultados obtenidos fueron medidos con las métricas de precisión, recall, f1-score y exactitud obteniendo 94.51%, 88.42%, 91.36% y 91.54% respectivamente

PRELIMINARY FINDINGS OF A POTENZIATED PIEZOSURGERGICAL DEVICE AT THE RABBIT SKULL

The number of available ultrasonic osteotomes has remarkably increased. In vitro and in vivo studies have revealed differences between conventional osteotomes, such as rotating or sawing devices, and ultrasound-supported osteotomes (Piezosurgery®) regarding the micromorphology and roughness values of osteotomized bone surfaces. Objective: the present study compares the micro-morphologies and roughness values of osteotomized bone surfaces after the application of rotating and sawing devices, Piezosurgery Medical® and Piezosurgery Medical New Generation Powerful Handpiece. Methods: Fresh, standard-sized bony samples were taken from a rabbit skull using the following osteotomes: rotating and sawing devices, Piezosurgery Medical® and a Piezosurgery Medical New Generation Powerful Handpiece. The required duration of time for each osteotomy was recorded. Micromorphologies and roughness values to characterize the bone surfaces following the different osteotomy methods were described. The prepared surfaces were examined via light microscopy, environmental surface electron microscopy (ESEM), transmission electron microscopy (TEM), confocal laser scanning microscopy (CLSM) and atomic force microscopy. The selective cutting of mineralized tissues while preserving adjacent soft tissue (dura mater and nervous tissue) was studied. Bone necrosis of the osteotomy sites and the vitality of the osteocytes near the sectional plane were investigated, as well as the proportion of apoptosis or cell degeneration. Results and Conclusions: The potential positive effects on bone healing and reossification associated with different devices were evaluated and the comparative analysis among the different devices used was performed, in order to determine the best osteotomes to be employed during cranio-facial surgery

MRI of foetal development

Foetal MRI represents a non-invasive imaging technique that allows detailed visualisation of foetus in utero and the maternal structure. This thesis outlines the quantitative imaging techniques used to investigate the effect of maternal diabetes and maternal smoking on foetal development at 1.5 Tesla. The effect of maternal diabetes on placental blood flow and foetal growth was studied. The placental images were acquired using Echo Planar Imaging and blood flow was measured using Intra Voxel Incoherent Motion. The results indicate that peak blood flow in the basal plate and chorionic plate increases across gestation in both normal and diabetic pregnancies. Conversely, diffusion in the whole placenta decreases across gestation, with a more pronounced decrease in diabetic placentae. Following this, a method was developed to use a Tl weighted fat suppressed MRI scan to quantify foetal fat images in-utero. In addition, HAlf Fourier Single-shot Turbo spin Echo (HASTE) and balanced Fast Field Echo (bFFE) were used to acquire images encompassing the whole foetus in three orthogonal planes. These scans were used to measure foetal volume, foetal length and shoulder width. The data shows that foetal fat volume and intra-abdominal fat were increased in foetuses of diabetic mothers at third trimester. The HASTE and bFFE sequences were also used to study the effect of maternal smoking on foetal development. Here, foetal organ volumes, foetal and placental volume, shoulder width and foetal length were measured using a semiautomatic approach based on the concept of edge detection and a stereological method, the Cavalieri technique. The data shows that maternal smoking has significant negative effect on foetal organ growth and foetal growth, predominantly foetal kidney and foetal volume. The work described here certainly has a great potential in non-invasive assessment of abnormal placental function and can be used to study foetal development

Abstracts from the 50th European Society of Human Genetics Conference: Posters

International audienc