Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin's structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient's medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease's complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models' opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.
Varicella zoster infection is one of the self-limiting viral infections during childhood and dengue fever is an endemic infection in Malaysia, which commonly occurs in the form of nonspecific febrile illness at the initial stage. It is rare for the two viral infections to occur simultaneously. A case of dengue fever without warning sign in a five-year old girl was reported, with early symptoms of fever and vesicular rashes. She was clinically diagnosed with varicella zoster infection during the first visit. Surprisingly, she remained febrile even on day six of illness despite no new vesicular lesions on her skin. Due to suspicion of another infection, follow-up investigation was done and revealed isolated thrombocytopenia. This finding was confirmed with positive NS1Ag. A case of rare dengue fever concomitant with varicella zoster infection was reported.
We report a case of unilateral acute retinal necrosis (ARN) with marked vitritis and retinal necrosis leading to retinal breaks following chicken pox successfully treated with intravenous acyclovir followed by oral acyclovir, orbital floor triamcinolone injections to contain the inflammation, and barrier laser therapy to secure the retinal breaks with good visual outcome. This case is unusual in its severity and the novel use orbital floor triamcinolone therapy to contain ARN inflammation.