Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Contributions to the segmentation of dermoscopic images

Tese de mestrado. Mestrado em Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201

Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortal- ity. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state- of-the-art segmentation methods for comparison. The results of the experi- mental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy signi- cantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classication, achieving promising results for melanoma detection

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

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

Statistical techniques applied to the automatic diagnosis of dermoscopic images

An image based system implementing a well‐known diagnostic method is disclosed for the automatic detection of melanomas as support to clinicians. The software procedure is able to recognize automatically the skin lesion within the digital image, measure morphological and chromatic parameters, carry out a suitable classification for the detection of structural dermoscopic criteria provided by the 7‐Point Check. Original contribution is referred to advanced statistical techniques, which are introduced at different stages of the image processing, including the border detection, the extraction of low‐level features and scoring of high order features (namely dermoscopic criteria). The proposed approach is experimentally tested with reference to a large image set of pigmented lesions