This paper presents an advanced approach for foetal brain abnormalities diagnostic by integrating significant biometric features in the identification process. In foetal anomaly diagnosis, manual evaluation of foetal behaviour in ultrasound images is a subjective, slow and error-prone task, especially in the preliminary treatment phases. The effectiveness of this appearance is strictly subject to the attention and the experience of gynaecologists. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of the foetal head in order to identify pregnancy behaviour. Indeed, we propose a computerised diagnostic method based on morphological characteristics and a supervised classification method to categorise subjects into two groups: normal and affected cases. The presented method is validated on a real integrated microcephaly and dolichocephaly cases. The studied database contains the same gestational age of both normal and abnormal foetuses. The results show that the use of a support vector machine (SVM) classifier is an effective way to enhance recognition and detection for rapid and accurate foetal head diagnostic.

Dizziness is a frequent syndrome of peripheral vestibular lesions. Identification of nystagmus disorder can be a useful sign to discriminate between diverse vestibular diseases. Through the use of videonystagmography (VNG) device accomplished in the clinical practice of ENT department, the assessment of the rotation eye movement response supplies objective, consistent and precise measurements in the therapeutic scheme. In fact, vestibular dysfunctions introduce an important variety in their features which includes different complications for common VNG examination method. This work introduces a new scheme to reach the classification of eye movement signals from optokinetic and caloric VNG tests. The proposed method offers quantitative assessment and uniform characteristics of recurrent-included disease. The rotation angle of eye movements is classified into two classes of vestibular diseases by the use of deep convolutional Neural Network (CNN) technique. The proposed approach is validated on three different categories: a factual incorporated meniere, neurite and healthy subjects. The employed VNG dataset contain patients admitted into both Charles Nicolle and La Rabta hospitals of Tunis. Compared to previous works, the results demonstrate that the proposed CNN-based method is efficient in enhancing dizziness analysis.

The Vestibulo-ocular response VOR is characterized by a smooth pursuit eye movements in one direction, called slow phase of ocular nystagmus, interrupted by resetting saccades fast phase of nystagmus in the other direction. Recording of ocular nystagmus during vestibular tests does not quantify the true response of the vestibulo-ocular reflex (VOR). In order to extract the real VOR, our study is focused on nystagmus analysis using videonystagmography (VNG) technique based on measuring amplitude vibration of eyeball movement. The effectiveness of this attendance is severely topic to the attention and the experience of ENT doctors. In this case, automatic methods of image analysis offer the possibility of obtaining a homogeneous, objective and above all fast diagnosis of vestibular disorder. In this paper, a fully automatic system based on nystagmus parameter analysis using a pupil detection algorithm is proposed. After a segmentation stage, a deep neural Network based classification method is applied on 90 eye movement recordings from videonystagmography (VNG) containing two types of peripheral vestibular disorders and normal patients. Experimental results obtained after several simulation, show the efficiency of the proposed methodology when compared with other classification methods.