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A survey on cancer detection via convolutional neural networks: Current challenges and future directions
P Sharma, DR Nayak, BK Balabantaray, M Tanveer… - Neural Networks, 2024 - Elsevier
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
Deep CNN and deep GAN in computational visual perception‐driven image analysis
Computational visual perception, also known as computer vision, is a field of artificial
intelligence that enables computers to process digital images and videos in a similar way as …
intelligence that enables computers to process digital images and videos in a similar way as …
Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy
Brain tumor classification is a challenging task in the field of medical image processing. The
present study proposes a hybrid method using Neutrosophy and Convolutional Neural …
present study proposes a hybrid method using Neutrosophy and Convolutional Neural …
White blood cells detection and classification based on regional convolutional neural networks
White blood cells (WBC) are important parts of our immune system and they protect our body
against infections by eliminating viruses, bacteria, parasites and fungi. There are five types …
against infections by eliminating viruses, bacteria, parasites and fungi. There are five types …
Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features
Necessary screenings must be performed to control the spread of the COVID‐19 in daily life
and to make a preliminary diagnosis of suspicious cases. The long duration of pathological …
and to make a preliminary diagnosis of suspicious cases. The long duration of pathological …
Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification
At present times, COVID-19 has become a global illness and infected people has increased
exponentially and it is difficult to control due to the non-availability of large quantity of testing …
exponentially and it is difficult to control due to the non-availability of large quantity of testing …
TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach
Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
applications, such as postoperative assessment, surgical planning, and pathological …
applications, such as postoperative assessment, surgical planning, and pathological …
An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine
Super-resolution, which is one of the trend issues of recent times, increases the resolution of
the images to higher levels. Increasing the resolution of a vital image in terms of the …
the images to higher levels. Increasing the resolution of a vital image in terms of the …
A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine
F Özyurt - Soft Computing, 2020 - Springer
White blood cell (WBC) test is used to diagnose many diseases, particularly infections,
ranging from allergies to leukemia. A physician needs clinical experience to detect and …
ranging from allergies to leukemia. A physician needs clinical experience to detect and …
A new approach for brain tumor diagnosis system: single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network
Magnetic resonance imaging (MRI) images can be used to diagnose brain tumors. Thanks
to these images, some methods have so far been proposed in order to distinguish between …
to these images, some methods have so far been proposed in order to distinguish between …