94 research outputs found

    A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

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    Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

    A model for the detection of breast cancer using machine learning and thermal images in a mobile environment

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    Breast cancer is the most common cancer amongst women and one of the deadliest. Various modalities exist which image the breasts, all with a focus on early detection; thermography is one such method. It is a non-invasive test, which is safe and can be used for a wide variety of breast densities. It functions by analysing thermal patterns captured via an infrared camera of the surface of the breast. Advances in infrared and mobile technology enable this modality to be mobile based; allowing a high degree of portability at a lower cost. Furthermore, as technology has improved, machine learning has played a larger role in medical practices by offering unbiased, consistent, and timely second opinions. Machine learning algorithms are able to classify medical images automatically if offered in the correct format. This study aims to provide a model, which integrates breast cancer detection, thermal imaging, machine learning, and mobile technology. The conceptual model is theorised from three literature studies regarding: identifiable aspects of breast cancer through thermal imaging, the mobile ecosystem, and classification using machine learning algorithms. The model is implemented and evaluated using an experiment designed to classify automatically thermal breast images of the same quality that mobile attachable thermal cameras are able to capture. The experiment contrasts various combinations of segmentation methods, extracted features, and classification algorithms. Promising results were shown in the experiment with a high degree of accuracy obtained. The successful results obtained from the experimentation process validates the feasibility of the model

    Implementing decision tree-based algorithms in medical diagnostic decision support systems

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    As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

    Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome

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    [ES] El síndrome de dolor regional complejo (SDRC) es un trastorno de dolor crónico debilitante que suele afectar a una extremidad, y se caracteriza por su compleja e incomprendida fisiopatología subyacente, lo que supone un reto para su diagnóstico y tratamiento. Para evitar el deterioro de la calidad de vida de los pacientes, la consecución de un diagnóstico y tratamiento tempranos marca un punto de inflexión. Entre los diferentes tratamientos, los bloqueos simpáticos lumbares (BSLs) tienen como objetivo aliviar el dolor y reducir algunos signos simpáticos de la afección. Este procedimiento intervencionista se lleva a cabo inyectando anestesia local alrededor de los ganglios simpáticos y, hasta ahora, se realiza frecuentemente bajo el control de diferentes técnicas de imagen, como los ultrasonidos o la fluoroscopia. Dado que la termografía infrarroja (TIR) ha demostrado ser una herramienta eficaz para evaluar la temperatura de la piel, y teniendo en cuenta el efecto vasodilatador que presentan los anestésicos locales inyectados, se ha considerado el uso de la IRT para la evaluación de los BSLs. El objetivo de esta tesis es, estudiar la capacidad de la TIR como una técnica complementaria para la evaluación de la eficacia en la ejecución de los BSLs. Para cumplir este objetivo, se han realizado tres estudios implementando la TIR en pacientes diagnosticados de SDRC de miembros inferiores sometidos a BSLs. El primer estudio se centra en la viabilidad de la TIR como herramienta complementaria para la evaluación de la eficacia ejecución de los BSLs. Cuando se realizan los BSLs, la colocación correcta de la aguja es crítica para llevar realizar el procedimiento técnicamente correcto y, en consecuencia, para lograr los resultados clínicos deseados. Para verificar la posición de la aguja, tradicionalmente se han utilizado técnicas de imagen, sin embargo, los BSLs bajo control fluoroscópico no siempre aseguran su exacta ejecución. Por este motivo, se han aprovechado las alteraciones térmicas inducidas por los anestésicos locales y se han evaluado mediante la TIR. Así, cuando en las imágenes infrarrojas se observaron cambios térmicos en la planta del pie afectado tras la inyección de lidocaína, se consideró que el BSL era exitoso. El segundo estudio trata del análisis cuantitativo de los datos térmicos recogidos en el entorno clínico a partir de diferentes parámetros basados en las temperaturas extraídas de ambos pies. Según los resultados, para predecir adecuadamente los BSLs exitosos, se deberían analizar las temperaturas de las plantas de los pies durante los primeros cuatro minutos tras la inyección del anestésico local. Así, la aplicación de la TIR en el entorno clínico podría ser de gran ayuda para evaluar la eficacia de ejecución de los BSLs mediante la evaluación de las temperaturas de los pies en tiempo real. Por último, el tercer estudio aborda el análisis cuantitativo mediante la implementación de herramientas de machine learning (ML) para evaluar su capacidad de clasificar automáticamente los BSLs. En este estudio se han utilizado una serie de características térmicas extraídas de las imágenes infrarrojas para evaluar cuatro algoritmos de ML para tres momentos diferentes después del instante de referencia (inyección de lidocaína). Los resultados indican que los cuatro modelos evaluados presentan buenos rendimientos para clasificar automáticamente los BSLs entre exitosos y fallidos. Por lo tanto, la combinación de parámetros térmicos junto con de clasificación ML muestra ser eficaz para la clasificación automática de los procedimientos de BSLs. En conclusión, el uso de la TIR como técnica complementaria en la práctica clínica diaria para la evaluación de los BSLs ha demostrado ser totalmente eficaz. Dado que es un método objetivo y relativamente sencillo de implementar, puede permitir que los médicos especialistas en dolor identifiquen los bloqueos realizados fallidos y, en consecuencia, puedan revertir esta situación.[CA] La síndrome de dolor regional complex (SDRC) és un trastorn de dolor crònic debilitant que sol afectar una extremitat, i es caracteritza per la seua complexa i incompresa fisiopatologia subjacent, la qual cosa suposa un repte per al seu diagnòstic i tractament. Per a evitar la deterioració de la qualitat de vida dels pacients, la consecució d'un diagnòstic i tractament primerencs marca un punt d'inflexió. Entre els diferents tractaments , els bloquejos simpàtics lumbars (BSLs) tenen com a objectiu alleujar el dolor i reduir alguns signes simpàtics de l'afecció. Aquest procediment intervencionista es duu a terme injectant anestèsia local al voltant dels ganglis simpàtics i, fins ara, es realitza freqüentment sota el control de diferents tècniques d'imatge, com els ultrasons o la fluoroscopia. Atés que la termografia infraroja (TIR) ha demostrat ser una eina eficaç per a avaluar la temperatura de la pell, i tenint en compte l'efecte vasodilatador que presenten els anestèsics locals injectats, s'ha considerat l'ús de la TIR per a l'avaluació dels BSLs. L'objectiu d'aquesta tesi és, estudiar la capacitat de la TIR com una tècnica complementària per a l'avaluació de l'eficàcia en l'execució dels BSLs. Per a complir aquest objectiu, s'han realitzat tres estudis implementant la TIR en pacients diagnosticats de SDRC de membres inferiors sotmesos a BSLs. El primer estudi avalua la viabilitat de la TIR com a eina complementària per a l'analisi de l'eficàcia en l'execució dels BSLs. Quan es realitzen els BSLs, la col·locació correcta de l'agulla és crítica per a dur a terme el procediment tècnicament correcte i, en conseqüència, per a aconseguir els resultats clínics desitjats. Per a verificar la posició de l'agulla, tradicionalment s'han utilitzat tècniques d'imatge, no obstant això, els BSLs baix control fluoroscòpic no sempre asseguren la seua exacta execució. Per aquest motiu, s'han aprofitat les alteracions tèrmiques induïdes pels anestèsics locals i s'han avaluat mitjançant la TIR. Així, quan en les imatges infraroges es van observar canvis tèrmics en la planta del peu afectat després de la injecció de lidocaIna, es va considerar que el BSL era exitós. El segon estudi tracta de l'anàlisi quantitativa de les dades tèrmiques recollides en l'entorn clínic a partir de diferents paràmetres basats en les temperatures extretes d'ambdós peus. Segons els resultats, per a predir adequadament l'execució exitosa d'un BSL, s'haurien d'analitzar les temperatures de les plantes dels peus durant els primers quatre minuts després de la injecció de l'anestèsic local. Així, l'implementació de la TIR en l'entorn clínic podria ser de gran ajuda per a avaluar l'eficàcia d'execució dels BSLs mitjançant l'avaluació de les temperatures dels peus en temps real. El tercer estudi aborda l'anàlisi quantitativa mitjançant la implementació d'eines machine learning (ML) per a avaluar la seua capacitat de classificar automàticament els BSLs. En aquest estudi s'han utilitzat una sèrie de característiques tèrmiques extretes de les imatges infraroges per a avaluar quatre algorismes de ML per a tres moments diferents després de l'instant de referència (injecció de lidocaïna). Els resultats indiquen que els quatre models avaluats presenten bons rendiments per a classificar automàticament els BSLs en exitosos i fallits. Per tant, la combinació de paràmetres tèrmics juntament amb models de classificació ML mostra ser eficaç per a la classificació automàtica dels procediments de BSLs. En conclusió, l'ús de la TIR com a tècnica complementària en la pràctica clínica diària per a l'avaluació dels BSLs ha demostrat ser totalment eficaç. Atés que és un mètode objectiu i relativament senzill d'implementar, pot ajudar els metges especialistes en dolor a identificar els bloquejos realitzats fallits i, en conseqüència, puguen revertir aquesta situació.[EN] Complex regional pain syndrome (CRPS) is a debilitating chronic pain condition that usually affects one limb, and it is characterized by its misunderstood underlying pathophysiology, resulting in both challenging diagnosis and treatment. To avoid the patients' impairment quality of life, the achievement of both an early diagnosis and treatment marks a turning point. Among the different treatment approaches, lumbar sympathetic blocks (LSBs) are addressed to alleviate the pain and reduce some sympathetic signs of the condition. This interventional procedure is performed by injecting local anaesthetic around the sympathetic ganglia and, until now, it has been performed under different imaging techniques, including the ultrasound or the fluoroscopy approaches. Since infrared thermography (IRT) has proven to be a powerful tool to evaluate skin temperatures and taking into account the vasodilatory effects of the local anaesthetics injected in the LSB, the use of IRT has been considered for the LSBs assessment. Therefore, the purpose of this thesis is to evaluate the capability of IRT as a complementary assessment technique for the LSBs procedures performance. To fulfil this aim, three studies have been conducted implementing the IRT in patients diagnosed with lower limbs CRPS undergoing LSBs. The first study focuses on the feasibility of IRT as a complementary assessment tool for LSBs performance, that is, for the confirmation of the proper needle position. When LSBs are performed, the correct needle placement is critical to carry out the procedure technically correct and, consequently, to achieve the desired clinical outcomes. To verify the needle placement position, imaging techniques have traditionally been used, however, LSBs under radioscopic guidance do not always ensure an exact performance. For this reason, the thermal alterations induced by the local anaesthetics, have been exploited and assessed by means of IRT. Thus, the LSB procedure was considered successfully performed when thermal changes within the affected plantar foot were observed in the infrared images after the lidocaine injection. The second study deals with the quantitative analysis of the thermal data collected in the clinical setting through the evaluation of different temperature-based parameters extracted from both feet. According to the results, the proper LSB success prediction could be achieved in the first four minutes after the block through the evaluation of the feet skin temperatures. Therefore, the implementation of IRT in the clinical setting might be of great help in assessing the LSBs performance by evaluating the plantar feet temperatures in real time. Finally, the third study addresses the quantitative analysis by implementing machine learning (ML) tools to assess their capability to automatically classify LSBs. In this study, a set of thermal features retrieved from the infrared images have been used to evaluate four ML algorithms for three different moments after the baseline time (lidocaine injection). The results indicate that all four models evaluated present good performance metrics to automatically classify LSBs into successful and failed. Therefore, combining infrared features with ML classification models shows to be effective for the LSBs procedures automatic classification. In conclusion, the use of IRT as a complementary technique in daily clinical practice for LSBs assessment has been evidenced entirely effective. Since IRT is an objective method and it is not very demanding to perform, it is of great help for pain physicians to identify failed procedures, and consequently, it allow them to reverse this situation.Cañada Soriano, M. (2022). Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181699TESI

    Deep Learning in Medical Image Analysis

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    The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

    The Technological Emergence of AutoML: A Survey of Performant Software and Applications in the Context of Industry

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    With most technical fields, there exists a delay between fundamental academic research and practical industrial uptake. Whilst some sciences have robust and well-established processes for commercialisation, such as the pharmaceutical practice of regimented drug trials, other fields face transitory periods in which fundamental academic advancements diffuse gradually into the space of commerce and industry. For the still relatively young field of Automated/Autonomous Machine Learning (AutoML/AutonoML), that transitory period is under way, spurred on by a burgeoning interest from broader society. Yet, to date, little research has been undertaken to assess the current state of this dissemination and its uptake. Thus, this review makes two primary contributions to knowledge around this topic. Firstly, it provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial. Secondly, it motivates and outlines a framework for assessing whether an AutoML solution designed for real-world application is 'performant'; this framework extends beyond the limitations of typical academic criteria, considering a variety of stakeholder needs and the human-computer interactions required to service them. Thus, additionally supported by an extensive assessment and comparison of academic and commercial case-studies, this review evaluates mainstream engagement with AutoML in the early 2020s, identifying obstacles and opportunities for accelerating future uptake

    Proceedings of ICMMB2014

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    Machine Learning in Medical Image Analysis

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    Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging. The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance (MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset. The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces
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