In this paper, deep belief learning network architecture (DBL) is proposed for medical image classification in a bid to improve the diagnostics of dermal melanoma as an alternative to traditional dermoscopy. Preprocessing was carried out by using a linear Gaussian filter by eliminating high-frequency artifacts and distortion. The
K
-means segmentation technique was used to extract the region of interest. The DBL network was then applied to the segmented image for classification. The DBL architecture disperses the weights and hyperparameters to all positions in an image, making it possible to scale to various image sizes. The effects of overfitting were mitigated for small datasets and were achieved by optimizing the proposed network. The algorithm works effectively by fine-tuning constraints. The results showed an increase in the accuracy between the proposed model and AlexNet and LeeNet for segmented images from 8% to 47%, respectively. Similarly, an increase for nonsegmented images was observed between 2% and 48%. An average reduction of 47.8% and 41.5% in error for both segmented and nonsegmented images was recorded for dermal images. The execution time also decreased in comparison with the other architectures averaged by 8-13%, since the weights were distributed only on the clustered regions in the segmented image, as compared to the whole image thus allowing the network to classify it faster with improved accuracy.
OBJECTIVES
To determine the frequency of surgical site infection in mesh repair for inguinal hernias. METHODOLOGY
This Descriptive observational study was carried out at the Surgical B unit of Hayatabad Medical Complex Peshawar from November 2021 to October 2022. A total of 179 patients were included in the study were given a single dose of antibiotics, i.e.1, gm Ceftriaxone, one hour before inguinal hernia mesh repair.RESULTS
A total of 179 patients aged between 30-60 years with a mean age of 45 years were enrolled. There were 98(54.7%) male while 81(45.3%) females. The frequency of wound infection was noted in 23 (12.8%) patients following mesh repair for inguinal hernia. Out of 23, most of the patients, 10(43.5%) had Medical redness & tenderness, 8(34.8%) patients had pus discharge from the wound side, and 5(21.7%) patients had wound site abscesses.CONCLUSION
Surgical site infection after mesh repair was higher than the internationally reported incidence. Establishing a baseline SSI rate for inguinal hernia repairs offers a useful benchmark for future studies and surgical programs in these
The successful ability to conduct underwater transportation using multiple autonomous underwater vehicles (AUVs) is important for the commercial sector to undertake precise underwater installations on large modules, whilst for the military sector it has the added advantage of improved secrecy for clandestine operations. The technical requirements are the stability of the payload and internal collision avoidance while keeping track of the desired trajectory considering the underwater effects. Here, a leader-follower formation control strategy was developed and implemented on the transportation system of AUVs. PID controllers were used for the vehicles and a linear feedback controller for maintaining the formation. A Kalman Filter (KF) was designed to estimate the full state of the leader under disturbance, noise and limited sensor readings. The results demonstrate that though the technical requirements are met, the thrust oscillations under disturbance and noise produce the undesired heading angles.
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