Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
Pakistan Navy Engineering College (PNEC)（巴基斯坦海军工程学院）；National University of Sciences & Technology (NUST)（国家科技大学）；Electronics and Power Engineering (EPE)（电子与动力工程系）
Muhammad Ali Farooq, Muhammad Aatif Mobeen Azhar, Rana Hammad Raza
Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin cancer classification. Computer aided diagnosis helps physicians and dermatologists to obtain a second opinion for proper analysis and treatment of skin cancer. Precise segmentation of the cancerous mole along with surrounding area is essential for proper analysis and diagnosis. This paper is focused towards the development of improved ALDS framework based on probabilistic approach that initially utilizes active contours and watershed merged mask for segmenting out the mole and later SVM and Neural Classifier are applied for the classification of the segmented mole. After lesion segmentation, the selected features are classified to ascertain that whether the case under consideration is melanoma or non-melanoma. The approach is tested for varying datasets and comparative analysis is performed that reflects the effectiveness of the proposed system.
In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fus…
Indian Institute of Science（印度科学院）；College of Engineering Trivandrum（特里凡得琅工程学院）
The revolutionary developments in the field of supervised machine learning have paved way to the development of CAD tools for assisting doctors in diagnosis. Recently, the former has been employed in the prediction of neurological disorders such as Alzheimer's disease. We propose a CAD (Computer Aided Diagnosis tool for differentiating neural lesions caused by CVA (Cerebrovascular Accident) from the lesions caused by other neural disorders by using Non-negative Matrix Factorisation (NMF) and Haralick features for feature extraction and SVM (Support Vector Machine) for pattern recognition. We a…
Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the targeted regions, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models. In this study, we investigate feasibility of applying a random projection algorithm to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. We assemble a retrospective dataset involving 1,487 cases of mamm…
Univ Lyon（里昂大学）；INSA‐Lyon（里昂国立应用科学学院）；Université Claude Bernard Lyon 1（里昂第一大学）；UJM-Saint Etienne（圣艾蒂安让·莫奈大学）；CNRS（法国国家科学研究中心）；Inserm（法国国家健康与医学研究院）；CREATIS UMR 5220（CREATIS联合研究单位5220）；U1206（U1206研究单位）
Computer aided diagnosis (CAD) systems are designed to assist clinicians in various tasks, including highlighting abnormal regions in a medical image. A common approach consists in training a voxel-level binary classifier on a set of feature vectors extracted from normal and pathological areas in patients' scans. However, many pathologies (such as epilepsy) are characterized by lesions that may be located anywhere in the brain, have various shapes, sizes and texture. An adequate representation of such a heterogeneity requires a significant amount of annotated data which is a major issue in the…
This short report describes our submission to the ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection for Task1 and Task 3. This work has been accomplished by a team of researchers at the University of Dayton Signal and Image Processing Lab. Our proposed approach is computationally efficient are combines information from both deep learning and handcrafted features. For Task3, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. These features are utilized as inputs to a decision-making model that…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心图像科学研究所）；Department of Biomedical Engineering, Eindhoven University of Technology（埃因霍温理工大学生物医学工程系）
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predicti…
University of Michigan（密歇根大学）；Imbio LLC（Imbio有限责任公司）
Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analys…
Universidad de Granada（格拉纳达大学）；Memorial Sloan-Kettering Cancer Center（纪念斯隆-凯特琳癌症中心）；Medical University Vienna/AKH Wien（维也纳医科大学/维也纳总医院）；Florida State University（佛罗里达州立大学）
Non-mass enhancing lesions (NME) constitute a diagnostic challenge in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer Aided Diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from DCE-MRI dataset of …
Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms
Federal University of Espírito Santo (UFES)（圣埃斯皮里图联邦大学）；Secretary of Health of the Espírito Santo State（圣埃斯皮里图州卫生部）；University Hospital Cassiano Antônio Moraes (HUCAM)（卡西亚诺·安东尼奥·莫赖斯大学医院）
According to the World Health Organization, breast cancer is the main cause of cancer death among adult women in the world. Although breast cancer occurs indiscriminately in countries with several degrees of social and economic development, among developing and underdevelopment countries mortality rates are still high, due to low availability of early detection technologies. From the clinical point of view, mammography is still the most effective diagnostic technology, given the wide diffusion of the use and interpretation of these images. Herein this work we propose a method to detect and cla…
Interpretable Prostate Cancer Detection using a Small Cohort of MRI Images
Department of Computer Science, Boston University Metropolitan College（波士顿大学计算机科学系）；Department of Radiology, McMaster University（麦斯特大学放射科）；Department of Radiology, School of Medicine, 5th Azar Hospital, Golestan University of Medical Sciences（戈兰大学医学学院放射科，第五阿扎尔医院）；Department of Medical Imaging, Niagara Health System（尼亚加拉健康系统医学影像科）；Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Chulalongkorn University（朱拉隆梭大学精准医学与数字健康中心，生理学系）
GDN: A Stacking Network Used for Skin Cancer Diagnosis
School of Artificial Intelligence and automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Biosystem Engineering, Zhejiang University（浙江大学生物系统工程学院）；Computer Science Department, New York University（纽约大学计算机科学系）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）
A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification
Deakin University, Australia（德克萨斯大学）；School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia（数学、物理与计算学院，南方昆士兰大学，托沃布马）
