Colorectal multi-class image classification using deep learning models

Raju, Mallela Siva Naga; Rao, B. Srinivasa

doi:10.11591/eei.v11i1.3299

Cited by 13 publications

(7 citation statements)

References 23 publications

(23 reference statements)

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“…Publicly available datasets like those provided by the Open Access Series of Imaging Studies (OASIS)Alzheimer's Disease Neuroimaging Initiative (ADNI) Medical Image Computing and Computer-Assisted Intervention (MICCAI)and the Internet Brain Segmentation Repository (IBSR) are routinely used for the segmentation of brain MRI images and the diagnosis of AD [8], [9], [10]. In Table 1 we show the data set specifications for OASIS, ADNI, MICCAI, and IBSR.…”

Section: Mri Dataset For Brain Analysismentioning

confidence: 99%

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

Aparna¹

2023

IEEE Access

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The anatomical structure of the brain has been studied with the help of magnetic resonance imaging (MRI), which has been used to analyze numerous neurological diseases and define pathological areas. Early detection of Alzheimer's Disease (AD) patients is critical in order to implement preventative measures. Alzheimer's disease (AD) is the most common chronic disease in the elderly, with a high incidence rate. In recent years, deep learning has seen a lot of success in the medical image analysis. Brain diseases can be more accurately categorized using segmented MRI scans due to in-depth analyses of tissue architecture. Many, complex segmentation approaches have been presented for AD diagnosis. Since deep learning algorithms can yield effective results over a large data collection, they have received interest for use in segmenting the brain's structure and classifying AD. Consequently, the deep learning techniques are currently favored over machine learning techniques. We discuss how convolutional neural network concepts can be used to study brain anatomy in order to detect AD. New techniques, their results on open datasets, and the benefits of brain MRI segmentation for Alzheimer's disease categorization are discussed. In this article, the literature on Alzheimer's disease is briefly reviewed, and the possibility of Deep Learning to improve early diagnosis is discussed.INDEX TERMS Alzheimer's disease, brain analysis, classification techniques, deep learning techniques, image processing techniques.

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Section: Mri Dataset For Brain Analysismentioning

confidence: 99%

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

Aparna¹

2023

IEEE Access

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“…Raju et al [30] proposed a CNN architecture based on deep learning models for HI multiclass categorization. This discovery was made by employing three distinct deep learning models.…”

Section: -Related Workmentioning

confidence: 99%

An ensemble WideResNet learning-based approach for classification of multi-class colorectal cancer tissue types in histology images

Masrori,

Dadpay,

Rezaee

et al. 2023

Scientia Iranica

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Histopathology imaging (HI) plays a significant role in enhancing the prognosis of colorectal cancer (CRC), which ranks as the second leading cause of cancer-related deaths globally. Classifying colon cancer tissues with HI can be challenging due to differences in morphology, the presence of artifacts when recording microscopic images, and the lack of histological expertise. The use of WideResNet network structure as a novel method for identifying textures in HIs is proposed using deep feature maps extracted from direct HIs and correlation matrices inputs. By using HSV (Hue, Saturation and Value) space, the first step is to decrease various artifacts during HI recording. After designing the ensemble model, the suggested structure has been evaluated and validated using the datasets CRC-5000 and NCT-CRC-HE-100K. On the CRC data set, the ensemble model had accuracy of 98.71% and 99.13%. Deep ensemble learning performed better than current methods in terms of computational and temporal performance, according to the results. Our proposed method is generalizable and has been tested on many of unseen HIs. Based on their data, pathologists can classify unseen Whole Slide Images (WSIs) images using only their lesion classes. The proposed tissue analysis mechanism reliably predicts CRC when pathological images are accurate.

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“…A variety of factors can cause cancer; especially behavioral factors like alcohol and cigarette use, high BMI, as well as environmental toxins like radiation and UV rays, as well as hereditary and biological carcinogens. [9][10][11] However, the reason may differ from patient to patient. Bruising, bleeding, muscle pain, weight loss, breathing problems, a persistent cough, nausea, exhaustion, discomfort, and other symptoms are all common cancer signs.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

Raju

Rao

2022

Concurrency and Computation

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Lung and colon cancers are dangerous diseases that can grow in organs and create a negative impact on human life in certain cases. The histological detection of such malignancies is one of the most critical parts of optimal treatment. As a result, the important objective of this article is to create an effective computerized diagnosis system for identifying adenocarcinomas of the colon as well as, adenocarcinomas and squamous cell carcinomas of the lungs using digital histopathology images and the combination of deep and machine learning techniques. For this, an effective optimized hybrid deep and machine learning framework is developed. This framework consists of two stages. In the first stage, the features of lung and colon images are extracted by principle component analysis network. Then the effective classification is conducted based on extreme learning machine (ELM) with the rider optimization algorithm which classifies lung and colon cancer into five types. The empirical investigation shows that the classification results on the benchmark LC25000 dataset have improved significantly. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting various forms of lung and colon cancers.

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Colorectal multi-class image classification using deep learning models

Cited by 13 publications

References 23 publications

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

A Review on Alzheimer’s Disease Through Analysis of MRI Images Using Deep Learning Techniques

An ensemble WideResNet learning-based approach for classification of multi-class colorectal cancer tissue types in histology images

Lung and colon cancer classification using hybrid principle component analysis network‐extreme learning machine

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