Development of automated diagnostic system for skin cancer: Performance analysis of neural network learning algorithms for classification

Melanoma is the most deathly of all skin cancers but early diagnosis can ensure a high degree of survival. Early diagnosis is one of the greatest challenges due to lack of experience of general practitioners (GPs). In this paper we present a clinical decision support system designed for general practitioners, aimed at saving time and resources in the diagnostic process. Segmentation, pattern recognition, and change detection are the important steps in our approach. This paper also investigates the performance of Artificial Neural Network (ANN) learning algorithms for skin cancer diagnosis. The capabilities of three learning algorithms i.e. Levenberg-Marquardt (LM), Resilient Back propagation (RP), Scaled Conjugate Gradient (SCG) algorithms in differentiating melanoma and benign lesions are studied and their performances are compared. The results show that Levenberg-Marquardt algorithm was quick and efficient in figuring out benign lesions with specificity 95.1%, while SCG algorithm gave better results in detecting melanoma at the cost of more number of epochs with sensitivity 92.6%. © 2014 Springer International Publishing Switzerland

A case-based reasoning approach to GBM evolution

GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013