arXiv subjects

Muhammed Talo

Publications and source records attributed to Muhammed Talo.

4 recordsLinked to original sources

arXiv2019

An Automated Deep Learning Approach for Bacterial Image Classification

Automated recognition and classification of bacteria species from microscopic images have significant importance in clinical microbiology. Bacteria classification is usually carried out manually by biologists using different shapes and morphologic characteristics of bacteria species. The manual taxonomy of bacteria types from microscopy images is time-consuming and a challenging task for even experienced biologists. In this study, an automated deep learning based classification approach has been proposed to classify bacterial images into different categories. The ResNet-50 pre-trained CNN architecture has been used to classify digital bacteria images into 33 categories. The transfer learning technique was employed to accelerate the training process of the network and improve the classification performance of the network. The proposed method achieved an average classification accuracy of 99.2%. The experimental results demonstrate that the proposed technique surpasses state-of-the-art methods in the literature and can be used for any type of bacteria classification tasks.

q-bio.QM↗

arXiv2019

Automated Classification of Histopathology Images Using Transfer Learning

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.

cs.CV↗

arXiv2018

Naming a Channel with Beeps

We consider a communication channel in which the only possible communication mode is transmitting beeps, which reach all the nodes instantaneously. Nodes are anonymous, in that they do not have any individual identifiers. The algorithmic goal is to randomly assign names to the nodes in such a manner that the names make a contiguous segment of positive integers starting from $1$. We give a Las Vegas naming algorithm for the case when the number of nodes $n$ is known, and a Monte Carlo algorithm for the case when the number of nodes $n$ is not known. The algorithms are provably optimal with respect to the expected time $O(n\log n)$, the number of used random bits $O(n\log n)$, and the probability of error.

cs.DC↗

arXiv2016

Anonymous Processors with Synchronous Shared Memory

We consider synchronous distributed systems in which anonymous processors communicate by shared read-write variables. The goal is to have all the processors assign unique names to themselves. We consider the instances of this problem determined by whether the number $n$ is known or not, and whether concurrently attempting to write distinct values into the same memory cell is allowed or not, and whether the number of shared variables is a constant independent of $n$ or it is unbounded. For known $n$, we give Las Vegas algorithms that operate in the optimum expected time, as determined by the amount of available shared memory, and use the optimum $O(n\log n)$ expected number of random bits. For unknown $n$, we give Monte Carlo algorithms that produce correct output upon termination with probabilities that are $1-n^{-Ω(1)}$, which is best possible when terminating almost surely and using $O(n\log n)$ random bits.

cs.DC↗