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To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and…
Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. Material and…
Brain tumour segmentation is an essential task in medical image processing. Early diagnosis of brain tumours plays a crucial role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of…
The new era of artificial intelligence demands large-scale ultrafast hardware for machine learning. Optical artificial neural networks process classical and quantum information at the speed of light, and are compatible with silicon…
$\bf{Purpose:}$ The goal of this study was (i) to use artificial intelligence to automate the traditionally labor-intensive process of manual segmentation of tumor regions in pathology slides performed by a pathologist and (ii) to validate…
In medical imaging, most of the image registration methods implicitly assume a one-to-one correspondence between the source and target images (i.e., diffeomorphism). However, this is not necessarily the case when dealing with pathological…
In this paper we discuss a new method for detecting leukemia in microscopic blood smear images using deep neural networks to diagnose leukemia early in blood. leukemia is considered one of the most dangerous mortality causes for a human…
Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at early stage plays a key role in successful prognosis and…
Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the…
Deep learning techniques have greatly benefited computer-aided diagnostic systems. However, unlike other fields, in medical imaging, acquiring large fine-grained annotated datasets such as 3D tumour segmentation is challenging due to the…
In this study, a morphological differentiation-based method has been introduced which employs temperature distribution on the tissue surface to detect brain tumor's malignancy. According to the common tumor CT scans, two different scenarios…
Brain tumor analysis in MRI images is a significant and challenging issue because misdiagnosis can lead to death. Diagnosis and evaluation of brain tumors in the early stages increase the probability of successful treatment. However, the…
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival rates. Manual interpretation of Magnetic Resonance Imaging (MRI) scans is…
Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present…
Medical image segmentation is a relevant problem, with deep learning being an exponent. However, the necessity of a high volume of fully annotated images for training massive models can be a problem, especially for applications whose images…
Tumor segmentation from magnetic resonance imaging (MRI) data is an important but time consuming manual task performed by medical experts. Automating this process is a challenging task because of the high diversity in the appearance of…
Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more…
The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning…
Medical image analysis has emerged as an essential element of contemporary healthcare, facilitating physicians in achieving expedited and precise diagnosis. Recent breakthroughs in deep learning, a subset of artificial intelligence, have…
Accurate detection of brain tumors could save lots of lives and increasing the accuracy of this binary classification even as much as a few percent has high importance. Neural Gas Networks (NGN) is a fast, unsupervised algorithm that could…