Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients' long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. The primary objective of this study was to maximize the input feature information by combining multiple deep models in the first phase, and then to avoid noisy and redundant information by downsampling the feature set, using a novel evolutionary feature selection technique, in the second phase. By maintaining the integrity of the original feature space, the proposed idea generated highly discriminant feature information. Recent deep models, including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2, were employed in our study, for the purpose of feature extraction. Additionally, transfer learning was leveraged, to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models was combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection, using a novel entropy-controlled gray wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques was employed, initially to incorporate the feature vector with a high level of information and, subsequently, to eliminate redundant and irrelevant feature information. The effectiveness of our concept is supported by an assessment conducted on three benchmark dermoscopic datasets: PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation was conducted, including a rigorous comparison to established techniques in the field.

CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET.

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

Frequency of gall bladder carcinoma in patients with acute and chronic cholecystitis.

BACKGROUND: Gallbladder carcinoma is a relatively uncommon neoplasm which is more common in the seventh decade of life compared to that of cholelithiasis which is in the fourth decade. The objective of the study was to determine the frequency of carcinoma gall bladder in cases of acute and chronic cholecystitis particularly with reference to cholelithiasis. METHODS: This cross-sectional observational study was done in the department of Surgery at Fauji Foundation Hospital, Rawalpindi from 1st January, 2009 till 3 1st October, 2011. All 310 cholecystectomies performed during this period were included in this study. The specimens collected were macroscopically examined and sent for histopathology. All the reports were reviewed and the results recorded. RESULTS: Out of 310 cases, 27 (8.7%) had acute cholecystitis and 280 (91.2%) were chronic cholecystitis. 3 specimens got autolysed and therefore excluded from the study. 290 cases had cholelithiasis. 2 out of 290 calculous cases were found to have coexistent carcinoma gall bladder proven on histopathology. CONCLUSION: Frequency of carcinoma gallbladder associated with cholelithiasis discovered incidentally after cholecystectomy is very low in our patients.

Nosocomial uropathogens and their antibiotic sensitivity patterns in a tertiary referral teaching hospital in Rawalpindi, Pakistan.

BACKGROUND: Nosocomial urinary tract infections (NUTIs) are by definition not present at admission of a patient and are acquired during hospitalisation. The objective of this study was to study the uropathogens and their antibiotic sensitivity patterns in hospital acquired urinary tract infections presenting in a teaching hospital. METHODOLOGY: It was a retrospective descriptive study carried out at the Department of Pathology, Fauji Foundation Hospital, Rawalpindi, Pakistan, during the year 2009. Reports of urine culture and sensitivity performed during one year were retrospectively studied with a view to document various isolates and their antimicrobial sensitivity. RESULTS: Out of a total number of 1204 urine cultures submitted, 246 were found to have nosocomial urinary tract infections. Over all prevalence of nosocomial urinary tract infection in the examined reports was 20.43%. CONCLUSION: Nosocomial Urinary tract infections are common. Gram negative bacilli are most frequent uropathogens and are resistant to commonly used antibiotics. Fosfomycin followed by Gentamycin and Cefotaxime were the most effective antibiotics.