Stacked Cross Validation with Deep Features: A Hybrid Method for Skin Cancer Detection

Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively

An algorithm for characterizing skin moles using image processing and machine learning

Melanoma, the most serious type of skin cancer, forms in cells (melanocytes) that produce melanin, the pigment that gives color to the skin. There are low-income regions that lack specialized dermatologists, causing skin cancer to be diagnosed in advanced stages. In Peru, in high Andean communities with low resources, the problem is aggravated by the high incidence of ultraviolet radiation and lack of medical resources to make the diagnosis. Normally, mole images are obtained from dermatoscopes. The present work seeks to use mole images obtained from smartphones to make the classification of them as suspected or not suspected of being melanoma, by means of a feature extraction algorithm. The first step is to make color and lighting corrections. After this, the image is segmented using the K-Means algorithm, and we obtain the areas of the mole and skin. With the segmented mole we proceed to extract the main visual characteristics and then use classification algorithms such as support vector machine (SVM), random forest and naïve bayes, which obtained an accuracy of 0.9473, 0.7368 and 0.6842, respectively. These results show that it is possible to use images obtained from smartphones to develop a classification algorithm with 94.73% accuracy to detect melanoma in skin moles

Machine Learning Classification of Primary Tissue Origin of Cancer from DNA Methylation Markers

Cancer is one of the leading causes of death globally and was responsible for approximately 9.6 million deaths in 2018. One of the main reason for deaths from cancer is late-stage presentation and inaccessible diagnosis and treatment. Cancer often spreads from the part of the body where it started (primary site) to a different part of the body (metastatic site). Identifying the primary site of cancer plays a key role as it directs the appropriate treatment. Cancer which spreads needs the same treatment as its origin. Having this knowledge can help doctors to decide the type of treatment. All cancers begin when one or more genes in a cell mutate and create abnormal proteins which cause cells to multiply uncontrollably. Genes are present in the DNA of each cell in human body, and research shows that distinct and abnormal patterns in methylation of DNA are observed in case of cancers. DNA methylation is also considered as an early and fundamental step where normal tissue undergoes transformations. Since DNA methylation is tissue-specific and change with cell differentiation, methylation sites are good markers for identifying tissues of origin. In this thesis, we propose the use of machine learning techniques to identify the primary sites of cancers to increase the accuracy of diagnosis and treatment. For this purpose, we implemented various classification algorithms in machine learning like support vector machines, random forests classifier, decision trees, and K nearest neighbor classifier to classify the tumor samples into their tissue origin and compared these models using traditional machine learning metrics. The models are trained and tested on features extracted from the DNA methylation datasets maintained by The Cancer Genome Atlas (TCGA). The experimental results showed that support vector machines could predict the primary sites with 95% training accuracy. The model gave 86% accuracy when tested on a completely independent dataset collected from Gene Expression Omnibus (GEO)

A Non-Invasive Interpretable Diagnosis of Melanoma Skin Cancer Using Deep Learning and Ensemble Stacking of Machine Learning Models

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in the detection of skin lesions. Still, due to image occlusions and imbalanced datasets, the accuracies have been compromised so far. In this paper, we introduce an interpretable method for the non-invasive diagnosis of melanoma skin cancer using deep learning and ensemble stacking of machine learning models. The dataset used to train the classifier models contains balanced images of benign and malignant skin moles. Hand-crafted features are used to train the base models (logistic regression, SVM, random forest, KNN, and gradient boosting machine) of machine learning. The prediction of these base models was used to train level one model stacking using cross-validation on the training set. Deep learning models (MobileNet, Xception, ResNet50, ResNet50V2, and DenseNet121) were used for transfer learning, and were already pre-trained on ImageNet data. The classifier was evaluated for each model. The deep learning models were then ensembled with different combinations of models and assessed. Furthermore, shapely adaptive explanations are used to construct an interpretability approach that generates heatmaps to identify the parts of an image that are most suggestive of the illness. This allows dermatologists to understand the results of our model in a way that makes sense to them. For evaluation, we calculated the accuracy, F1-score, Cohen\u27s kappa, confusion matrix, and ROC curves and identified the best model for classifying skin lesions

Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks

Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. Results All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. Conclusion These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance