Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lac...
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Colorectal polyps are benign lesions that develop in the colon and can progress to cancer if left untreated. Clinical observations from medical images are often preferred over computational results due to the lack of trust in the machine learning models, thereby posing serious challenge for the explainability of the results. In order to computationally diagnose colorectal polyps from cancerous images and explain the results, we propose a Layer-wise eXplainable ResUNet++ (LeXNet++) framework for segmentation of the cancerous images, followed by layer-wise explanation of the results. We utilize a publicly accessible dataset that contains of 612 raw images with a resolution of 256×256×3 and an additional 612 clinically annotated and labeled images with a resolution of 256×256×1, which includes the infected region. The LeXNet++ framework comprises of three components—encoder, decoder and the bridge. The encoder and the decoder components each comprise of four layers. Each of the four layers in the encoder and the decoder comprises of 14 and 11 internal sub-layers, respectively. Among the sub-layers of the encoder and the decoder, there are three 3×3 convolutional layers with an additional 3×3 convolution-transpose layer in the decoder. The output of each of the sub-layers has been explained through heatmap generation after each iteration which have been further explained. The encoder and the decoder are connected by the bridge which comprises of three sub-layers. The results obtained from these three sub-layers have also been explained to inculcate trust in the findings. In this study, we have used three models to segment the images, namely UNet, ResUNet, and proposed LeXNet++. LeXNet++ exhibited the best result among the three models in terms of performance;hence, only LeXNet++ was explained layer-wise. Apart from explanation of the results fetched in this study, the performance of the proposed explainable model has been observed to be 2% greater than the existing poly
The proliferation of deepfake technology poses a significant challenge to the authenticity of digital content. This research explores the application of multimodal fusion techniques to enhance deepfake detection accur...
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Object detection in surveillance systems leverages advanced deep learning techniques to enhance security measures through real-time analysis of dynamic video feeds. This project integrates the YOLOv5 model for detecti...
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Advanced techniques for body part detection and marker-less sensor-based cue selection is needed for automated human posture estimation (A-HPE) systems to efficiently identify complicated activity movements. The compl...
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As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectivene...
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Farmers are increasingly adopting Smart farming worldwide, leveraging various advanced technologies. Artificial Intelligence (AI) is instrumental in driving the evolution of smart agriculture applications. Internet of...
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The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a stud...
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This paper introduces an innovative method for automating lip-reading, with a specific focus on the Marathi language. Lip-reading plays a crucial role in aiding those with hearing impairments, but automating it presen...
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Web scraping, an automated process that extracts data from websites, is a powerful tool that faces several challenges. Existing scrapers often entail drawbacks, such as subscription fees and limited accessibility for ...
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This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory T...
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