CA : A Cancer Journal for Clinicians

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control.

Beta-band oscillations--signalling the status quo?

To improve the performance of particle image velocimetry in measuring instantaneous velocity fields, direct cross-correlation of image fields can be used in place of auto-correlation methods of interrogation of double- or multiple-exposure recordings. With improved speed of photographic recording and increased resolution of video array detectors, cross-correlation methods of interrogation of successive single-exposure frames can be used to measure the separation of pairs of particle images between successive frames. By knowing the extent of image shifting used in a multiple-exposure and by a priori knowledge of the mean flow-field, the cross-correlation of different sized interrogation spots with known separation can be optimized in terms of spatial resolution, detection rate, accuracy and reliability.

Theory of cross-correlation analysis of PIV images : Image analysis as measuring technique in flows

Anthropometry of the head and face

Melanoma spreads through metastasis, and therefore, it has been proved to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the cancer; early detection and intervention of melanoma implicate higher chances of cure. Clinical diagnosis and prognosis of melanoma are challenging, since the processes are prone to misdiagnosis and inaccuracies due to doctors’ subjectivity. Malignant melanomas are asymmetrical, have irregular borders, notched edges, and color variations, so analyzing the shape, color, and texture of the skin lesion is important for the early detection and prevention of melanoma. This paper proposes the two major components of a noninvasive real-time automated skin lesion analysis system for the early detection and prevention of melanoma. The first component is a real-time alert to help users prevent skinburn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module, which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system uses PH2 Dermoscopy image database from Pedro Hispano Hospital for the development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including benign, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the benign, atypical, and melanoma images with accuracy of 96.3%, 95.7%, and 97.5%, respectively.

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

In speech analysis, the voiced-unvoiced decision is usually performed in extracting the information from the speech signals. In this paper, two methods are performed to separate the voiced and unvoiced parts of the speech signals. These are zero crossing rate (ZCR) and energy. In here, we evaluated the results by dividing the speech sample into some segments and used the zero crossing rate and energy calculations to separate the voiced and unvoiced parts of speech. The results suggest that zero crossing rates are low for voiced part and high for unvoiced part where as the energy is high for voiced part and low for unvoiced part. Therefore, these methods are proved effective in separation of voiced and unvoiced speech.

Voiced/Unvoiced Decision for Speech Signals Based on Zero-Crossing Rate and Energy.

Voiced/Unvoiced Decision for Speech Signals Based on Zero-Crossing Rate and Energy

Diabetic retinopathy is the most common diabetic eye disease and a leading cause of blindness in the world. Diagnosis of diabetic retinopathy at an early stage can be done through the segmentation of blood vessels of the retina. In this work, the performance of descriptive statistical features in retinal vessel segmentation is evaluated by using fuzzy logic, an artificial neural network classifier (ANN), a support vector machine (SVM), and classifier fusion. Newly constructed eight features are formed by statistical moments. Mean and median measurements of image pixels’ intensity values in four directions, horizontal, vertical, up-diagonal, and down-diagonal, are calculated. Features, F1, F2, F3, and F4 are calculated as the mean values and F5, F6, F7, and F8 are calculated as the median values of a processed pixel in each direction. A fuzzy rule-based classifier, an ANN, a SVM, and a classifier fusion are designed. The publicly available DRIVE and STARE databases are used for evaluation. The fuzzy classifier achieved 93.82% of an overall accuracy, 72.28% of sensitivity, and 97.04% of specificity. For the ANN classifier, 94.2% of overall accuracy, 67.7% of sensitivity, and 98.1% of specificity are achieved on the DRIVE database. For the STARE database, the fuzzy classifier achieved 92.4% of overall accuracy, 75% of sensitivity, and 94.3% of specificity. The ANN classifier achieved the overall accuracy, sensitivity, and specificity as 94.2%, 56.9%, and 98.4%, respectively. Although the overall accuracy of the SVM is calculated lower than the fuzzy and the ANN classifiers, it achieved higher sensitivity rates. Designed classifier fusion achieved the best performance among all by using the proposed statistical features. Its overall accuracy, sensitivity, and specificity are calculated as 95.10%, 74.09%, 98.35% for the DRIVE and 95.53%, 70.14%, 98.46 for the STARE database, respectively. The experimental results validate that the descriptive statistical features can be employed in retinal vessel segmentation and can be used in rule-based and supervised classifiers.

Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion

Melanoma incidence rates have been increasing for the past three decades. Most people diagnosed with non-melanoma skin cancer have higher chances to cure, but melanoma survival rates are low comparing to other skin cancer types. It is important to note that one in five Americans will develop skin cancer in their lifetime, and on average, one American dies from skin cancer every hour. A system to prevent this type of skin cancer is being awaited and is highly in-demand. Early detection of melanoma is one of the major factors to increase the chance of cure significantly. Malignant melanomas are asymmetrical and have irregular borders with rages and notched edges, so analyzing the shape of the skin lesion is important for melanoma early detection and prevention. In this paper, we introduce an automated skin lesion segmentation and analysis for early detection and prevention based on color and shape geometry. The system further incorporates other feature sets such as color to determine the lesion type. In our proposed system, we used PH2 Dermoscopy image database from Pedro Hispano Hospital for the development of our system and for testing purposes. This image database contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases. Our approach of analyzing the shape geometry and the color will be helpful to detect atypical lesions before it grows and becomes a melanoma case.

Buket D. Barkana

Papers

Noninvasive Real-Time Automated Skin Lesion Analysis System for Melanoma Early Detection and Prevention

Voiced/Unvoiced Decision for Speech Signals Based on Zero-Crossing Rate and Energy.

Voiced/Unvoiced Decision for Speech Signals Based on Zero-Crossing Rate and Energy

Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion

Automated skin lesion analysis based on color and shape geometry feature set for melanoma early detection and prevention