Electrocardiogram (ECG) is one of the most important and effective tools in clinical routine to assess the cardiac arrhythmias. In this research higherorder spectral estimations, bispectrum and third-order cumulants, are evaluated, saved, and pre-trained using convolutional neural networks (CNN) algorithm. CNN is transferred in this study to carry out automatic ECG arrhythmia diagnostics after employing the higher-order spectral algorithms. Transfer learning strategies are applied on pre-trained convolutional neural network, namely AlexNet and GoogleNet, to carry out the final classification. Five different arrhythmias of ECG waveform are chosen from the MIT-BIH arrhythmia database to evaluate the proposed approach. The main contribution of this study is to utilize the pre-trained convolutional neural networks with a combination of higher-order spectral estimations of arrhythmias ECG signal to implement a reliable and applicable deep learning classification technique. The Highest average accuracy obtained is 97.8 % when using third cumulants and GoogleNet. As is evident from these results, the proposed approach is an efficient automatic cardiac arrhythmia classification method and provides a reliable recognition system based on well-established CNN architectures instead of training a deep CNN from scratch.

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Blindness usually comes from two main causes, glaucoma and diabetes. Robust mass screening is performed for diagnosing, such as screening that requires a cost-effective method for glaucoma and diabetic retinopathy and integrates well with digital medical imaging, image processing, and administrative processes. For addressing all these issues, we propose a novel low-cost automated glaucoma and diabetic retinopathy diagnosis system, based on features extraction from digital eye fundus images. This paper proposes a diagnosis system for automated identification of healthy, glaucoma, and diabetic retinopathy. Using a combination of local binary pattern features, Gabor filter features, statistical features, and color features which are then fed to an artificial neural network and support vector machine classifiers. In this work, the classifier identifies healthy, glaucoma, and diabetic retinopathy images with an accuracy of 91.1%,92.9%, 92.9%, and 92.3% and sensitivity of 91.06%, 92.6%, 92.66%, and 91.73% and specificity of 89.83%, 91.26%, 91.96%, and 89.16% for ANN, and an accuracy of 90.0%,92.94%, 95.43%, and 97.92% and sensitivity of 89.34%, 93.26%, 95.72%, and 97.93% and specificity of 95.13%, 96.68%, 97.88%, and 99.05% for SVM, based on 5, 10, 15, and 31 number of selected features. The proposed system can detect glaucoma, diabetic retinopathy and normal cases with high accuracy and sensitivity using selected features, the performance of the system is high due to using of a huge fundus database.

White blood cells analysis is generally performed for helping specialists in evaluating a wide range of hematic pathologies such as acquired immune deficiency syndrome (AIDS), blood cancer (leukemia) and other related diseases. Segmentation, Counting, and classification of leukocytes or white blood cells (WBC) in the peripheral blood samples images provide informative data about the samples. Therefore, performing them in the most efficient way is very important in the hematological analysis procedure. Unfortunately, the traditional manual segmentation method is very tedious, time-consuming and provides inaccurate results due to the human factor. Hence, a computer-aided segmentation and classification system is needed to make the process both fast and accurate. In this paper, a new and completely automated system for different types of WBCs segmentation is proposed. The system is evaluated on peripheral blood smears from whole slide images based on color space transformation. In the segmentation process of the WBC, image enhancement techniques were applied on saturation frame of HSV color space and a simple thresholding technique was used to find the brightest object. Moreover, three types of features were extracted from the segmented WBCs which are morphological, statistical, and textural. Order of features is performed using the principal component analysis (PCA). Then, the performance of three classifiers probabilistic neural network (PNN) and support vector machine (SVM) and Random Forest Tree is obtained. In total, the results show that the proposed method is accurate and sufficient to be applied in hematological laboratories. The average accuracy of segmentation was 98.98% and classification was 99.6%.

White blood cells analysis is generally performed for helping specialists in evaluating a wide range of hematic pathologies such as acquired immune deficiency syndrome (AIDS), blood cancer (leukemia) and other related diseases. Segmentation, Counting, and classification of leukocytes or white blood cells (WBC) in the peripheral blood samples images provide informative data about the samples. Therefore, performing them in the most efficient way is very important in the hematological analysis procedure. Unfortunately, the traditional manual segmentation method is very tedious, time-consuming and provides inaccurate results due to the human factor. Hence, a computer-aided segmentation and classification system is needed to make the process both fast and accurate. In this paper, a new and completely automated system for different types of WBCs segmentation is proposed. The system is evaluated on peripheral blood smears from whole slide images based on color space transformation. In the segmentation process of the WBC, image enhancement techniques were applied on saturation frame of HSV color space and a simple thresholding technique was used to find the brightest object. Moreover, three types of features were extracted from the segmented WBCs which are morphological, statistical, and textural. Order of features is performed using the principal component analysis (PCA). Then, the performance of three classifiers probabilistic neural network (PNN) and support vector machine (SVM) and Random Forest Tree is obtained. In total, the results show that the proposed method is accurate and sufficient to be applied in hematological laboratories. The average accuracy of segmentation was 98.98% and classification was 99.6%.