Identifying Surgical Instruments in Pedagogical Cataract Surgery Videos through an Optimized Aggregation Network

Instructional cataract surgery videos are crucial for ophthalmologists and trainees to observe surgical details repeatedly. This paper presents a deep learning model for real-time identification of surgical instruments in these videos, using a custom dataset scraped from open-access sources. Inspired by the architecture of YOLOV9, the model employs a Programmable Gradient Information (PGI) mechanism and a novel Generally-Optimized Efficient Layer Aggregation Network (Go-ELAN) to address the information bottleneck problem, enhancing Minimum Average Precision (mAP) at higher Non-Maximum Suppression Intersection over Union (NMS IoU) scores. The Go-ELAN YOLOV9 model, evaluated against YOLO v5, v7, v8, v9 vanilla, Laptool and DETR, achieves a superior mAP of 73.74 at IoU 0.5 on a dataset of 615 images with 10 instrument classes, demonstrating the effectiveness of the proposed model.

Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone

According to the World Malaria Report of 2022, 247 million cases of malaria and 619,000 related deaths were reported in 2021. This highlights the predominance of the disease, especially in the tropical and sub-tropical regions of Africa, parts of South-east Asia, Central and Southern America. Malaria is caused due to the Plasmodium parasite which is circulated through the bites of the female Anopheles mosquito. Hence, the detection of the parasite in human blood smears could confirm malarial infestation. Since the manual identification of Plasmodium is a lengthy and time-consuming task subject to variability in accuracy, we propose an automated, computer-aided diagnostic method to classify malarial thin smear blood cell images as parasitized and uninfected by using the ResNet50 Deep Neural Network. In this paper, we have used the pre-trained ResNet50 model on the open-access database provided by the National Library of Medicine's Lister Hill National Center for Biomedical Communication for 150 epochs. The results obtained showed accuracy, precision, and recall values of 98.75%, 99.3% and 99.5% on the ResNet50(proposed) model. We have compared these metrics with similar models such as VGG16, Watershed Segmentation and Random Forest, which showed better performance than traditional techniques as well.

Classification of Melanocytic Nevus Images using BigTransfer (BiT)

Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods.

A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi

Skin cancer is a fatal manifestation of cancer. Unrepaired deoxyribo-nucleic acid (DNA) in skin cells, causes genetic defects in the skin and leads to skin cancer. To deal with lethal mortality rates coupled with skyrocketing costs of medical treatment, early diagnosis is mandatory. To tackle these challenges, researchers have developed a variety of rapid detection tools for skin cancer. Lesion-specific criteria are utilized to distinguish benign skin cancer from malignant melanoma. In this study, a comparative analysis has been performed on five Transfer Learning-based techniques that have the potential to be leveraged for the classification of melanocytic nevi. These techniques are based on deep convolutional neural networks (DCNNs) that have been pre-trained on thousands of open-source images and are used for day-to-day classification tasks in many instances.