Melanoma Detection Using Mobile Technology and Feature-Based Classification Techniques

Melanoma is one of the most dangerous types of skin cancer in terms of the ratio of death cases. Probability of death increases when it is diagnosed late. However, it is possible to treat melanoma successfully when diagnosed in its early stages. One of the most common medical methods for diagnosing melanoma is the ABCD (Asymmetry, Border irregularity, Color, and Diameter) method that involves the measurement of four features of skin lesions. The main disadvantage of this method is that estimation error and subjectivity affects the accuracy of diagnosis, especially when performed by non-specialists. Scarcity of specialists makes the problem worse. This has led to the development of computer systems to help in melanoma diagnosis. However, while most computer systems can achieve high accuracy with adequate speed, they have problems in the usability and flexibility. The emergence of smart phones with increasing image capture and processing capabilities has made it more possible to use such devices to perform medical image analysis such as the diagnosis of melanoma. Our research work combines existing melanoma diagnosis method and the image capture and processing capabilities of smart phones to achieve fast, affordable, easily available and highly accurate melanoma diagnosis. In this work, we propose a complete smart phone application to capture, and process an image of the suspicious region of the skin in order to estimate its probability of being melanoma. The system can use historical cases to improve its diagnosis accuracy. The system was tested on 164 sample images. 14 images were not well-captured and could not be diagnosed, while the remaining 150 cases were successfully processed. In each of these 150 images, the lesion was correctly segmented and their ABCD feature set extracted. Diagnosis accuracy of the analyzed images ranged between 88%-94 with best results using SVM classifier, and worst is the KNN classifier.الميلانوما أحد أخطر أنواع سرطان الجلد من حيث نسبة عدد الوفيات الى حالات الاصابة ،تزداد الخطورة في الحالات التي يتم معالجتها في مراحل متأخرة ، ولكن يمكن علاج الميلانوما بنجاح اذا تم اكتشاف المرض في مراحله الاولى ، لذلك هناك العديد من الطرق المتبعة لتشخيص المرض في المراحل الأولى لتوجيه المريض الى الطبيب المختص مباشرة في حالة الشك في وجود المرض ، اشهر هذه الطرق هي طريقة ABCD، وأهم العقبات التي تواجه هذه الطريقة هي عدم دقة التنفيذ من الاشخاص غير المتخصصين ، لأن اعتماد الطريقة على خصائص مثل الحجم واللون والشكل يجعلها عرضة للكثير من التقدير والنسبية في التشخيص مما يفقد هذه الطريقة الكثير من الدقة في النتائج ، لذلك تم خلال الاعوام السابقة العمل على العديد من الانظمة المحوسبة التي تسعي الى المساعدة في التشخيص وتقليل نسبة الخطأ، وأتمتة التشخيص بحيث لا يخضع للتقدير الشخصي ، ومع بداية ظهور الاجهزة الذكية والتي تجاوز استخدامها حدود التواصل ليتم استخدامها في التقاط الصور بدقة عالية ومعالجة البيانات بكفاءة والتواصل مع شبكة الانترنت، كان اتجاه للاستفادة من مرونة الأجهزة الذكية ودقتها في التقاط الصور ومعالجتها من اجل توفير انظمة لتشخيص الأمراض المختلفة، وكان نصيب من هذه الابحاث والتطبيقات لتسهيل عملية تشخيص الميلانوما، ورغم توفر العديد من الأنظمة والابحاث الا أن النتائج لا تزال في المراحل الاولى حيث هناك العديد من العقبات، فلا تزال دقة هذه الانظمة لا تصل الى المستوى المطلوب مما يجعلها في بعض الحالات خطر على حياة المريض اذا تم تشخيص الحالات المصابة بشكل سلبي، كما ان امكانات معالجة الصور وتخزين البيانات وتصنيفها يعتبر من المجالات الجديدة والتي لم يتم اختبارها بشكل كافي في الابحاث السابقة ولم يتم التعامل مع الامكانات الجديدة للهواتف الذكية ، وخلال فترة قريبة كان مثلا تحليل كميات كبيرة من البيانات على الهواتف الذكية ومعالجتها وتصنيفها من الامور الغير ممكنة والتي اصبحت الان من الامور الممكنة في ظل تطور وحدات التخزين والمعالجة بشكل كبير للهواتف الذكية. لذلك جاء هذا العمل لمواصلة الجهد المبذول في توفير حل لمشكلة تشخيص مرض الميلانوما بشكل دقيق وفعال ومرن باستخدام الهواتف الذكية ، حيث تم استخدام امكانات الهواتف الذكية في التقاط الصور ومعالجتها بالإضافة لإمكانية تخزين المعلومات واستخدامها في التصنيف وتوقع الحالات الجديدة المصابة بالمرض، وتم بناء واختبار نظام كامل لتحقيق ذلك ، وقد كانت نتائج العمل مُرضِية جدا حيث تم فحص البرنامج على عينة مكونة من 164 صورة حيث نجح البرنامج في مرحلة معالجة الصورة في معالجة 150 صورة وعزل منطقة الآفة من أصل 164 صورة كما ذكرنا، علما ان نسبة النجاح في معالجة الصور تم تحسينها لتجاوز الأخطاء اثناء التقاط الصور من خلال استخدام واجهة تفاعلية للمستخدم ، اما في مرحلة التصنيف علي 150 صورة الناتجة من معالجة الصور فكانت دقة نتائج التصنيف بين 88-94% حسب نظام التصنيف المتبع ، وهي نتائج جيدة و يمكن البناء عليها في استخدام الموبايل في التشخيص الأولى لمرضى الميلانوم

Resolution invariant wavelet features of melanoma studied by SVM classifiers

This article refers to the Computer Aided Diagnosis of the melanoma skin cancer. We derive wavelet-based features of melanoma from the dermoscopic images of pigmental skin lesions and apply binary C-SVM classifiers to discriminate malignant melanoma from dysplastic nevus. The aim of this research is to select the most efficient model of the SVM classifier for various image resolutions and to search for the best resolution-invariant wavelet bases. We show AUC as a function of the wavelet number and SVM kernels optimized by the Bayesian search for two independent data sets. Our results are compatible with the previous experiments to discriminate melanoma in dermoscopy images with ensembling and feed-forward neural networks

MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant tumors from the body. This research develops an Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach. The proposed framework, called MFSNet (Multi-Focus Segmentation Network), uses differently scaled feature maps for computing the final segmentation mask using raw input RGB images of skin lesions. In doing so, initially, the images are preprocessed to remove unwanted artifacts and noises. The MFSNet employs the Res2Net backbone, a recently proposed convolutional neural network (CNN), for obtaining deep features used in a Parallel Partial Decoder (PPD) module to get a global map of the segmentation mask. In different stages of the network, convolution features and multi-scale maps are used in two boundary attention (BA) modules and two reverse attention (RA) modules to generate the final segmentation output. MFSNet, when evaluated on three publicly available datasets: $PH^2$, ISIC 2017, and HAM10000, outperforms state-of-the-art methods, justifying the reliability of the framework. The relevant codes for the proposed approach are accessible at https://github.com/Rohit-Kundu/MFSNe

Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

A comparison of edge detection methods for segmentation of skin lesions in mobile-phone-quality images

The world is faced with a rapidly increasing number of skin cancers every year. Melanoma is the most deadly type of skin cancer though it can be treated if it has been detected at an early stage. However, there is a shortage of dermatologists in rural areas. The increasing number of camera phones, together with improved coverage in rural areas gives some potential for tele-dermatology, whereby people with no local access to a dermatologist can send images of suspicious skin lesions to an expert for assessment. Merely relaying images to a human expert solves only part of the problem, there is still an acute shortage of experts whose time is limited. Computer Assisted Diagnosis (CADi) of lesions promises to reduce the workload of dermatologists by acting as an assistant. Current skin lesion CADi systems employ algorithms that are designed to run on a computer at a clinic. These clinic-based systems are limited when it comes to te/e-dermatology as they rely on a suitable quality image being sent in, and need to process a large number of arriving images. An alternative to this process, afforded by the growing capabilities of mobile phones, is to do some of the CADi processing on the phone which was used to take the image. This has the potential advantage that images can be evaluated for quality on the patient\u27s side, making it more convenient to take another image, rather than waiting for the clinic\u27s assessment. Distributing the processing to the patient\u27s phone also eases the workload on the clinic\u27s machine. The first step towards implementing skin lesion CADi is the segmentation of lesions from the image background; therefore for a mobile phone to perform CADi it is a pre-requisite that it would be able to perform this step. The study seeks to determine, for an existing skin lesion segmentation algorithm, whether it is practical to adapt for mobile phone use given the limitations of the mobile camera\u27s low resolution. The chosen algorithm depends on an edge detection step, and so an investigation will be made into edge detectors. Edge detectors are sensitive to their parameters, pixel size and lighting conditions - thus the parameters published for clinic-based systems which rely on high resolution cameras and custom lighting can not be expected ideal for mobile phone use. Experiments have shown that the approach from Xu et. al. (1999) can only apply on some types of images, which have unique background colour and distinctive from the lesion (foreground) colour

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Diagnóstico automático de melanoma mediante técnicas modernas de aprendizaje automático

The incidence and mortality rates of skin cancer remain a huge concern in many countries. According to the latest statistics about melanoma skin cancer, only in the Unites States, 7,650 deaths are expected in 2022, which represents 800 and 470 more deaths than 2020 and 2021, respectively. In 2022, melanoma is ranked as the fifth cause of new cases of cancer, with a total of 99,780 people. This illness is mainly diagnosed with a visual inspection of the skin, then, if doubts remain, a dermoscopic analysis is performed. The development of e_ective non-invasive diagnostic tools for the early stages of the illness should increase quality of life, and decrease the required economic resources. The early diagnosis of skin lesions remains a tough task even for expert dermatologists because of the complexity, variability, dubiousness of the symptoms, and similarities between the different categories among skin lesions. To achieve this goal, previous works have shown that early diagnosis from skin images can benefit greatly from using computational methods. Several studies have applied handcrafted-based methods on high quality dermoscopic and histological images, and on top of that, machine learning techniques, such as the k-nearest neighbors approach, support vector machines and random forest. However, one must bear in mind that although the previous extraction of handcrafted features incorporates an important knowledge base into the analysis, the quality of the extracted descriptors relies heavily on the contribution of experts. Lesion segmentation is also performed manually. The above procedures have a common issue: they are time-consuming manual processes prone to errors. Furthermore, an explicit definition of an intuitive and interpretable feature is hardly achievable, since it depends on pixel intensity space and, therefore, they are not invariant regarding the differences in the input images. On the other hand, the use of mobile devices has sharply increased, which offers an almost unlimited source of data. In the past few years, more and more attention has been paid to designing deep learning models for diagnosing melanoma, more specifically Convolutional Neural Networks. This type of model is able to extract and learn high-level features from raw images and/or other data without the intervention of experts. Several studies showed that deep learning models can overcome handcrafted-based methods, and even match the predictive performance of dermatologists. The International Skin Imaging Collaboration encourages the development of methods for digital skin imaging. Every year since 2016 to 2019, a challenge and a conference have been organized, in which more than 185 teams have participated. However, convolutional models present several issues for skin diagnosis. These models can fit on a wide diversity of non-linear data points, being prone to overfitting on datasets with small numbers of training examples per class and, therefore, attaining a poor generalization capacity. On the other hand, this type of model is sensitive to some characteristics in data, such as large inter-class similarities and intra-class variances, variations in viewpoints, changes in lighting conditions, occlusions, and background clutter, which can be mostly found in non-dermoscopic images. These issues represent challenges for the application of automatic diagnosis techniques in the early phases of the illness. As a consequence of the above, the aim of this Ph.D. thesis is to make significant contributions to the automatic diagnosis of melanoma. The proposals aim to avoid overfitting and improve the generalization capacity of deep models, as well as to achieve a more stable learning and better convergence. Bear in mind that research into deep learning commonly requires an overwhelming processing power in order to train complex architectures. For example, when developing NASNet architecture, researchers used 500 x NVidia P100s - each graphic unit cost from $5,899 to$7,374, which represents a total of $2,949,500.00 -$3,687,000.00. Unfortunately, the majority of research groups do not have access to such resources, including ours. In this Ph.D. thesis, the use of several techniques has been explored. First, an extensive experimental study was carried out, which included state-of-the-art models and methods to further increase the performance. Well-known techniques were applied, such as data augmentation and transfer learning. Data augmentation is performed in order to balance out the number of instances per category and act as a regularizer in preventing overfitting in neural networks. On the other hand, transfer learning uses weights of a pre-trained model from another task, as the initial condition for the learning of the target network. Results demonstrate that the automatic diagnosis of melanoma is a complex task. However, different techniques are able to mitigate such issues in some degree. Finally, suggestions are given about how to train convolutional models for melanoma diagnosis and future interesting research lines were presented. Next, the discovery of ensemble-based architectures is tackled by using genetic algorithms. The proposal is able to stabilize the training process. This is made possible by finding sub-optimal combinations of abstract features from the ensemble, which are used to train a convolutional block. Then, several predictive blocks are trained at the same time, and the final diagnosis is achieved by combining all individual predictions. We empirically investigate the benefits of the proposal, which shows better convergence, mitigates the overfitting of the model, and improves the generalization performance. On top of that, the proposed model is available online and can be consulted by experts. The next proposal is focused on designing an advanced architecture capable of fusing classical convolutional blocks and a novel model known as Dynamic Routing Between Capsules. This approach addresses the limitations of convolutional blocks by using a set of neurons instead of an individual neuron in order to represent objects. An implicit description of the objects is learned by each capsule, such as position, size, texture, deformation, and orientation. In addition, a hyper-tuning of the main parameters is carried out in order to ensure e_ective learning under limited training data. An extensive experimental study was conducted where the fusion of both methods outperformed six state-of-the-art models. On the other hand, a robust method for melanoma diagnosis, which is inspired on residual connections and Generative Adversarial Networks, is proposed. The architecture is able to produce plausible photorealistic synthetic 512 x 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problema domains. In this manner, the lack of data, the imbalance problems, and the overfitting issues are tackled. Finally, several convolutional modes are extensively trained and evaluated by using the synthetic images, illustrating its effectiveness in the diagnosis of melanoma. In addition, a framework, which is inspired on Active Learning, is proposed. The batch-based query strategy setting proposed in this work enables a more faster training process by learning about the complexity of the data. Such complexities allow us to adjust the training process after each epoch, which leads the model to achieve better performance in a lower number of iterations compared to random mini-batch sampling. Then, the training method is assessed by analyzing both the informativeness value of each image and the predictive performance of the models. An extensive experimental study is conducted, where models trained with the proposal attain significantly better results than the baseline models. The findings suggest that there is still space for improvement in the diagnosis of skin lesions. Structured laboratory data, unstructured narrative data, and in some cases, audio or observational data, are given by radiologists as key points during the interpretation of the prediction. This is particularly true in the diagnosis of melanoma, where substantial clinical context is often essential. For example, symptoms like itches and several shots of a skin lesion during a period of time proving that the lesion is growing, are very likely to suggest cancer. The use of different types of input data could help to improve the performance of medical predictive models. In this regard, a _rst evolutionary algorithm aimed at exploring multimodal multiclass data has been proposed, which surpassed a single-input model. Furthermore, the predictive features extracted by primary capsules could be used to train other models, such as Support Vector Machine