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Malignant melanoma is reported to be the deadliest of skin cancers. Therefore, early diagnosis is crucial for reducing of melanoma-related deaths. Medical Informatics uses the computer technology such as Computer Aided Diagnosis (CAD) for melanoma diagnostic. This paper presents computational intelligence approaches namely, Artificial Neural Network (ANN) and Adaptive-Network-based Fuzzy Inference...
The increased utilization of Computer Aided diagnosis (CAD) in clinical procedures has been very effective in discovering numerous abnormalities in human beings. CAD of lung nodules can be safely employed to validate the opinion of radiologists in discovering existence of nodules and assess the existence and severity of lung cancer. This paper provides a comprehensive review of the existing automated...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet This paper provide a broad review for most important algorithms used in the CAD application for lung tissue diagnostics and highlighted the performance of each distinctive algorithm. Moreover, ROC characteristics have been made for each selected algorithms (support...
Lung cancer is the foremost cause of death in many regions of the world. Early detection betters the chances of survival. PA chest radiography is the most commonly used diagnosis tool for detecting lung tumor, because it is cost effective and requires less radiation dose. Radiologists fail to detect nodule from PA chest radio graphs, at early stage because of complex anatomical structure present in...
Skin cancer is one of the most frequent cancers among human beings. Whereas, malignant melanoma is the most aggressive and deadly type of skin cancer, and its incidence has been quickly increasing over the last years. The detection of the malignant melanoma in its early stages with dermoscopic images reduces the mortality considerably, hence this a crucial issue for the dermatologists. However, their...
In computer-aided diagnosis of clustered microcalcifications (MCs), the individual MCs in a lesion need to be first detected prior to subsequent classification as being benign or malignant. However, owing to noise characteristics and patient variability, the detection accuracy is often adversely compromised by the occurrence of false-positives (FPs) or missed MCs in detection. To deal with difficulty,...
Breast cancer is the most common cancer in women worldwide. It is also the principle cause of death from cancer among women globally. Mammogram image is considered as the most reliable, low cost, and highly sensitive technique for detecting small lesions. Computer-aided diagnosis system (CAD) can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional...
Computer Aided Diagnosis (CAD) is one of the trusted methods in the field of medicine. CAD system assists the doctors for the diagnosis of diseases in higher degree of perfection within a short period of time. Now CAD is the most preferable method for the initial diagnosis of cancer using X-ray, CT, mammogram or MRI images. CAD works as an intermediate in between the radiologist and the input images...
This paper evaluated the performance of two-dimensional (2D) and 3D texture features from CT images on pulmonary nodules diagnosis using the large database LIDC-IDRI. Total of 905 nodules (422 malignant and 483 benign) with certain expert observer ratings of malignancy were extracted from the database based on the radiologists' painting boundaries. Feature analysis on the extracted nodules was not...
In this paper, a novel Computer-aided Diagnosis (CADx) system has been proposed for mass diagnosis in mammography images. Zernike moments are utilized as descriptors of shape and density characteristics in order to improve the overall accuracy. The input Regions of Interest (ROI) are segmented and subjected to some preprocessing stages. The outcome of preprocessing stage is a gray-scale image containing...
Computer Aided Diagnosis (CAD) system provides medical assistance by scanning digital images from computer tomography (CT) for suspicious masses and highlights the noticeable segments like presence of tumours, neural blockage etc. This paper, presents a scheme to improve the efficiency of existing CAD systems by proposing a feature extraction model which is carried out in two phases. First phase carries...
Breast cancer is reported to be the second deadliest cancer among cancerous woman. Statistics show that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of micro calcification in ones breast. However, micro calcification could be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided...
In this paper, a novel group of features have been introduced for diagnosing the masses in mammography images. The goal is increasing the performance of CADx algorithms as well as decreasing computational complexity. The proposed features are proper descriptors of mass margin which are called Fourier Transform of Radial Distance (FTRD). The input ROI has been segmented manually by expert radiologists...
In this paper, we designed a Computer-Aided-Diagnosis (CAD) system for lesion detection in breast MR images. The CAD process begins with analysis of MR images to detect the existence of lesion. If lesion exists, it is then coloured based on its type; benign, suspicious or malignant. Our CAD system enables better visualization of the lesions and improves accuracy as well as speed for breast cancer...
The development of computer-aided diagnosis (CAD) for breast magnetic resonance (MR) images has encountered some big challenges. One of these challenges is related to breast lesion segmentation. Accurate segmentation of breast lesions has a vital role in other consequent applications such as feature extraction. Since malignant breast lesions typically appear with irregular borders and shapes in MR...
Intelligent Computer Aided Diagnosis (CAD) Systems can be used for detecting Microcalcification (MC) clusters in digital mammograms at the early stage. CAD systems help radiologists in identifying tumor patterns in an efficient and faster manner than other detection methods. In this paper, we propose a new approach for detecting tumors in mammograms using Radial Basis Function Networks (RBFNN). Prior...
Computer aided diagnosis plays an important role in automatic detection of abnormal shadow area on CT images. The method of an automatic detection of lung nodules regions of interest (ROI) is presented in this paper. Firstly, the lung areas are segmented from CT images. Then, the initial ROI including nodules and blood vessels in lung areas are extracted by Top-hat filter. Thirdly, the second ROI...
Two-dimensional Principal component analysis (2DPCA) is widely used in face feature extraction and recognition as its lower-computational complexity comparing with principal component analysis (PCA). In this paper, we propose a feature extraction algorithm of pulmonary nodules based on 2DPCA with adaptive parameters. The cumulative variance proportion which is the histogram peak value of CT image...
Feature extracted from structural irregularity for skin lesion boundaries has a great significance in computer-aided diagnosis for melanomas. Based on previous work using local fractal dimension (local FD) for contour irregularity descriptions, the novelty of this paper focuses on: (1) Multi-scaled curvature analysis is used to acquire features of boundary irregularity (2) Feature differences from...
In this work we propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CAD). The traditional approach in CAD is to first train a pattern-classifier based on a set of existing training samples, and then apply this classifier to subsequent new cases. In our proposed approach, we will first apply image-retrieval to obtain a set of lesion images from...
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