Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art.
Background and Aim: Pregnancy associated skin diseases or changes can be physiological (hormonal), skin pre-existing disease, and development of dermatoses with new pregnancy. The specific skin dermatoses related to pregnancy involve eruption of pruritic skin in poorly defined heterogeneous group. The present study aimed to evaluate the specific dermatoses and skin disease affected by pregnancy. Methodology: This cross-sectional study was carried out on 226 pregnant women in the Department of Dermatology and Obstetrics and Gynecology of Dow University Hospital, Karachi from June 2021 to November 2021. All the patients were investigated for pregnancy associated cutaneous changes. The presence of any concomitant dermatoses that developed during pregnancy was investigated. Detailed clinical examination was performed on pregnant women with specific dermatoses of pregnancy regarding pattern, distribution, and morphology of lesions. All the pregnant women undergone through routinely blood investigation. Results: Of the total 226 pregnant women, the incidence of specific dermatosis during pregnancy was 27 (11.95%). Prurigo of pregnancy (5.8%) was the prevalent specific dermatosis of pregnancy. The incidence of intrahepatic cholestasis of pregnancy (ICP), pruritic urticarial papules and plaques of pregnancy (PUPP), and pruritic folliculitis of pregnancy (PFP) was 0.41%, 3.9%, and 0.17% respectively. All these skin diseases were caused by pregnancy in 11.95% of females. Conclusion: The present study found that Prurigo of pregnancy was the prevalent dermatosis of pregnancy that occurred in the multigravida second trimester. The rare dermatosis with no primary lesion was intrahepatic cholestasis of pregnancy. The course of pregnancy-associated disease changes, in turn, causes exacerbation like psoriasis, vitiligo, and dermatosis. Keywords: Specific Dermatoses, Skin disease, Pregnancy
Aim: To assess the efficacy of slit skin smear and fine-needle aspiration cytology (FNAC) for diagnosing cutaneous leishmaniasis while keeping histopathological analysis as a gold standard. Place and duration of study: From 6th Jan 2020 to 6th Jan 2021 at Bakhtawar Amin Medical & Dental College, Multan. Study design: A Cross-Sectional Study Methodology: In this study, a total of 180 patients were observed. In the slit skin smear technique, the Smear was fixed and stained with Leishman or Giemsa stain. For the FNAC technique, hematoxylin and eosin (H & E) stained smear slides were prepared which were examined under light power. For FNAC, the stained slides were examined under low (10x) and high power (40x) to examine cell mass and product formation and then under oil immersion lens (100x) for identifying the morphology of the parasite. Results: The diagnostic accuracy of slit skin smear was 44.44% and that of fine-needle aspiration cytology was 88.33%. Conclusion: Fine needle aspiration cytology has better diagnostic accuracy as compared to slit skin smear in diagnosing cutaneous leishmaniasis. Keywords: slit skin smear, fine needle aspiration cytology, cutaneous leishmaniasis, histopathology.
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