Deep learning techniques applied to skin lesion classification: a review

Skin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models.info:eu-repo/semantics/publishedVersio

Crops leaf diseases recognition: a framework of optimum deep learning features

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time

Deep learning applied to the classification of skin lesions

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáSkin cancer has been a global health issue and its diagnosis is a challenge in the medical field. Among all the types of skin cancer, melanoma is the worst and can be lethal if not early treated. The use of deep learning techniques, specifically, convolutional neural networks can help to improve the accuracy and speed up the classification of skin lesions. In this work, we aim to employ different image preprocessing techniques, various convolutional neural network models, data augmentation, and ensemble techniques to compare their results and provide an analysis of the data obtained. To achieve that, it was performed several experiments combining different image preprocessing techniques, which, paired with data augmentation strategies, aim to enhance the accuracy and reliability of the classification models. Additionally, three ensemble methods were tested to improve the classification systems’ robustness and reliability by gathering the strengths of each model. Our best result was the ensemble of EfficientNet-B2, EfficientNet-B5, and ResNeSt101 models with the application of data augmentation, and the combination of color constancy and hair removal techniques. This combined approach achieved a balanced accuracy of0.8132. By offering insights into the challenges faced, methodologies employed, and results obtained, this story aims to serve as a guide for researchers and practitioners aiming to advance the field of skin lesion classification using deep learning. Keywords: Deep Learning; Skin Lesion Classification; Image preprocessing.O câncer de pele é um problema de saúde global e seu diagnóstico é um desafio na área médica. Entre todos os tipos de câncer de pele, o melanoma é o pior e pode ser letal se não tratado precocemente. O uso de técnicas de deep learning, especificamente, redes neurais convolucionais, pode ajudar a melhorar a precisão e acelerar a classificação de lesões de pele. Neste trabalho, buscamos empregar diferentes técnicas de pré-processamento de imagens, vários modelos de redes neurais convolucionais, data augmentation e técnicas de ensemble para comparar seus resultados e fornecer uma análise dos dados obtidos. Para isso, foram realizados vários experimentos combinando diferentes técnicas de préprocessamento de imagens, que, combinadas com estratégias de data augmentation, visam melhorar a precisão e confiabilidade dos modelos de classificação. Além disso, três métodos de ensemble foram testados para melhorar a robustez e confiabilidade dos sistemas de classificação, reunindo os pontos fortes de cada modelo. Nosso melhor resultado foi o ensemble dos modelos EfficientNet-B2, EfficientNet-B5 e ResNeSt101 com a aplicação de data augmentation e a combinação de técnicas de color constancy e remoção de pelos. Esta abordagem alcançou uma balanced accuracy de 0,8132. Ao oferecer insights sobre as metodologias empregadas e resultados obtidos, este estudo visa servir como um guia para pesquisadores e profissionais que buscam avançar no campo da classificação de lesões cutâneas usando aprendizado profundo

Data-driven patient-specific breast modeling: a simple, automatized, and robust computational pipeline

Background: Breast-conserving surgery is the most acceptable option for breast cancer removal from an invasive and psychological point of view. During the surgical procedure, the imaging acquisition using Magnetic Image Resonance is performed in the prone configuration, while the surgery is achieved in the supine stance. Thus, a considerable movement of the breast between the two poses drives the tumor to move, complicating the surgeon's task. Therefore, to keep track of the lesion, the surgeon employs ultrasound imaging to mark the tumor with a metallic harpoon or radioactive tags. This procedure, in addition to an invasive characteristic, is a supplemental source of uncertainty. Consequently, developing a numerical method to predict the tumor movement between the imaging and intra-operative configuration is of significant interest. Methods: In this work, a simulation pipeline allowing the prediction of patient-specific breast tumor movement was put forward, including personalized preoperative surgical drawings. Through image segmentation, a subject-specific finite element biomechanical model is obtained. By first computing an undeformed state of the breast (equivalent to a nullified gravity), the estimated intra-operative configuration is then evaluated using our developed registration methods. Finally, the model is calibrated using a surface acquisition in the intra-operative stance to minimize the prediction error. Findings: The capabilities of our breast biomechanical model to reproduce real breast deformations were evaluated. To this extent, the estimated geometry of the supine breast configuration was computed using a corotational elastic material model formulation. The subject-specific mechanical properties of the breast and skin were assessed, to get the best estimates of the prone configuration. The final results are a Mean Absolute Error of 4.00 mm for the mechanical parameters E_breast = 0.32 kPa and E_skin = 22.72 kPa. The optimized mechanical parameters are congruent with the recent state-of-the-art. The simulation (including finding the undeformed and prone configuration) takes less than 20 s. The Covariance Matrix Adaptation Evolution Strategy optimizer converges on average between 15 to 100 iterations depending on the initial parameters for a total time comprised between 5 to 30 min. To our knowledge, our model offers one of the best compromises between accuracy and speed. The model could be effortlessly enriched through our recent work to facilitate the use of complex material models by only describing the strain density energy function of the material. In a second study, we developed a second breast model aiming at mapping a generic model embedding breast-conserving surgical drawing to any patient. We demonstrated the clinical applications of such a model in a real-case scenario, offering a relevant education tool for an inexperienced surgeon

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap