Yazar "Hafeez, Muhammad Adeel" seçeneğine göre listele

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    Brain tumor classification using MRI images and convolutional neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Hafeez, Muhammad Adeel; Kayasandık, Cihan Bilge; Doğan, Merve Yüşra
    The brain tumor has become one of the most prominent types of cancers affecting a huge population across the globe every year. It has the lowest life expectancy rate and the risk of death is highly associated with the type, shape, and location of the tumor. The Magnetic Resonance Imaging (MRI) is a strong tool to detect different brain lesions and is extensively used by radiologists and physicians. For the early and accurate diagnosis of the brain tumor using MRI, it is important to consider automated computer-assisted diagnosis which is more flexible and efficient. In this paper, we have proposed a Convolutional Neural Network (CNN) based approach for the classification of three types of brain tumors (meningiomas, gliomas, and pituitary tumors). A publicly available dataset that contains 3064 T1-weighted brain CE-MRI images collected from 233 patients has been used in the study. We propose a 15 layers CNN model for the classification of three types of brain tumors from the mentioned dataset. We obtained an accuracy, precision, recall, and f1-score of 98.6%, 99%, 98.3%, and 98.6% from our proposed model which is higher than previously reported results.
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    Rotten-fruit-sorting robotic arm: (Design of low complexity cnn for embedded system)
    (MDPI, 2022) Amin, Muhammad Ihtisham; Hafeez, Muhammad Adeel; Awais, Qasim
    Industrial Automation has revolutionized the processing industry due to its high accuracy, the time it saves, and its ability to work without tiring. Being the most fundamental part of automation machines, robotic arms are being used as a fundamental component in many types of domestic as well as commercial automation units. In this paper, we proposed a low-complexity convolutional neural network (CNN) model and successfully deployed it on a locally generated robotic arm with the help of a Raspberry Pi 4 module. The designed robotic arm can detect, locate, and classify (based on fresh or rotten) between three species of Mangos (Ataulfo, Alphonso, and Keitt), on a conveyor belt. We generated a dataset of about 6000 images and trained a three-convolutional-layer-based CNN. Training and testing of the network were carried out with MatLab, and the weighted network was deployed to an embedded environment (Raspberry Pi 4 module) for real-time classification. We reported a classification accuracy of 98.08% in the detection of fresh mangos and 95.75% in the detection of rotten mangos. For the designed robotic art, the achieved angle accuracy was 93.94% with a minor error of only 2°. The proposed model can be deployed in many food- or object-sorting industries as an edge computing application of deep learning.
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    rs-fMRI analysis using spatio-temporal sparse convolutional neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Yener, Fatma Muberr; Yıldız, Sultan; Hafeez, Muhammad Adeel; Kayasandık, Cihan Bilge; Doğan, Merve Yüsra
    Neuropsychiatric diseases such as Autism Spectrum Disorder (ASD) and Schizophrenia cause various behavioral and communication dysfunctions in human life. Resting state functional magnetic resonance imaging (rs-fMRI) is used to detect and characterize functional changes in the brain associated with these disorders. Machine learning methods are known to perform well in classifying fMRI images and have proven to have great potential in the field of computer aided diagnosis. In most of the previous studies, hand-crafted features have been used in fMRI analyzes and classifications to date. This prevents the system from being end-to-end and causes spatial or temporal information to be lost due to dimension reduction. The method presented in this study works end-to-end as well as being fed with an entire 4-dimensional fMRI sequence. It is faster than traditional convolutions and recurrent neural networks of the same size, thanks to the sparse convolutional layers that are the building blocks of the network. Experiments with schizophrenia and ASD fMRIs have shown similar performance to those in the literature, despite limited resources.