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Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model’s hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal $$91.2\%$$ 91.2 % , $$96\%$$ 96 % , $$69\%$$ 69 % , $$92.45\%$$ 92.45 % , 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems. Graphical abstract

Alopecia areata (AA) is an autoimmune form of nonscarring hair loss. The aim of the study was to assess the serum concentration of interferon gamma (IFN‐γ) and CD8 cell expression in lesional skin biopsies in correlation with the disease severity, activity, duration, and trichoscopic findings in patients with AA. The study included 30 patients with AA and 15 age‐ and sex‐matched healthy controls. Trichoscopy was performed and photographs were captured for the alopecic areas, and the enzyme‐linked immunosorbent assay technique was used for serum level of IFN‐γ assessment and immunohistochemistry for CD8 cells. The results obtained indicate that IFN‐γ serum level in patients was significantly higher than that of control subjects, and significantly correlated with the activity status and the duration of the disease. CD8+ T cells infiltrate intensity significantly correlated with severity. Yellow dots (YDs), vellus hair, black dot, and exclamation marks were the most common trichoscopic findings. The presence of black dots significantly correlated to the disease activity, duration, serum IFN‐γ, and CD8+ infiltrate intensity. The presence of YDs significantly correlated with the mean serum IFN‐γ level. Exclamation marks significantly correlated with the disease activity and the degree of CD8+ infiltrate. In conclusion, trichoscopy could be a reliable indicator of the IFN‐γ serum level and CD8+ T cell infiltrate intensity in AA patient.

Much effort is being made by the researchers in order to detect and diagnose diabetic retinopathy (DR) accurately automatically. The disease is very dangerous as it can cause blindness suddenly if it is not continuously screened. Therefore, many computers aided diagnosis (CAD) systems have been developed to diagnose the various DR grades. Recently, many CAD systems based on deep learning (DL) methods have been adopted to get deep learning merits in diagnosing the pathological abnormalities of DR disease. In this paper, we present a full based-DL CAD system depending on multi-label classification. In the proposed DL CAD system, we present a customized efficientNet model in order to diagnose the early and advanced grades of the DR disease. Learning transfer is very useful in training small datasets. We utilized IDRiD dataset. It is a multilabel dataset. The experiments manifest that the proposed DL CAD system is robust, reliable, and deigns promising results in detecting and grading DR. The proposed system achieved accuracy (ACC) equals 86%, and the Dice similarity coefficient (DSC) equals 78.45%.

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