Classification of Colon and Lung Cancer Through Analysis of Histopathology Images Using Deep Learning Models

Raju, Mallela Siva Naga; Rao, B. Srinivasa

doi:10.18280/isi.270613

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…The outputs of these 1000 categories were the results of these frozen fully-connected layers, which needed the use of the two-phase transfer learning approach. A new fully-connected layer, a SoftMax layer, and an output layer for four-class classification were required to replace them [27][28][29][30][31][32][33]…”

Section: Figure 4 Basic Architecture Of Proposed Methodologymentioning

confidence: 99%

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Aparna,

Rao

2023

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Alzheimer's disease, a debilitating neurological disorder, precipitates irreversible cognitive decline and memory loss, predominantly affecting individuals aged 65 years and above. The need for an automated system capable of accurately diagnosing and stratifying Alzheimer's disease into distinct stages is paramount for early intervention and management. However, existing deep learning methodologies are often hampered by protracted training times. In this study, a time-efficient approach incorporating a two-phase transfer learning technique is proposed to surmount this challenge. This method is particularly efficacious in the analysis of Magnetic Resonance Imaging (MRI) data for the identification of Alzheimer's disease. The proposed detection system employs two-phase transfer learning, augmented with fine-tuning for multi-class classification of brain MRI scans. This allows for the categorization of images into four distinct classes: Mild Dementia (MD), Moderate Dementia (MOD), Non-Dementia (ND), and Very Mild Dementia (VMD). The classification of Alzheimer's disease was conducted using various pre-trained deep learning models, including ResNet50V2, InceptionResNetV2, Xception, DenseNet121, VGG16, and MobileNetV2. Among the models tested, ResNet50V2 demonstrated superior performance, achieving a training classification accuracy of 99.35% and a testing accuracy of 99.25%. The results underscore the potential of the proposed method in delivering more accurate classifications than those obtained from extant models, thereby contributing to the early detection and stratification of Alzheimer's disease.

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Section: Figure 4 Basic Architecture Of Proposed Methodologymentioning

confidence: 99%

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Aparna,

Rao

2023

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“…However, with the increasing reliance on medical imaging data, radiologists are now facing significant challenges. To address this, artificial intelligence (AI) methodologies are rapidly evolving to bolster the potential for autonomous medical image assessment [11].…”

Section: Introductionmentioning

confidence: 99%

Bottleneck Feature-Based U-Net for Automated Detection and Segmentation of Gastrointestinal Tract Tumors from CT Scans

Gandikota,

Abirami,

Kumar

2023

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In today's medical landscape, an array of diagnostic techniques for cancer, leveraging imaging data, have become increasingly prevalent. This has posed a unique challenge for radiologists in the detection of Digestive System Cancer (DSC). This paper introduces the Bottleneck Feature-based U-Net, an innovative method designed for the automated detection and segmentation of the digestive system utilizing endoscopy. The U-Net design, previously proven successful for image segmentation tasks, is harnessed to its full potential in our proposed method. We have enhanced its performance by integrating a bottleneck feature extraction technique. The encoding U-Net is initially trained prior to the training of the BS U-Net, facilitating the procurement of encodings from label maps containing crucial anatomical information, such as shape and location. A Bottleneck Supervised (BS) U-Net is thus formed by pairing an encoding U-Net with a segmentation U-Net. Our proposed bottleneck feature in the U-Network enables the model to compress input data, an essential learning component. This compressed view of data retains vital information used for either reconstructing the input image or carrying out the segmentation process. In the current study, we put forth a bottleneck-based U-Net model tailored to perform gastrointestinal tract tumor segmentation. To train and test our method, we employed the comprehensive Kvasir dataset, which encompasses a wide range of digestive system images. We further tested the robustness and generalizability of our model through a thorough quantitative and qualitative analysis. The results underscore the versatility of the bottleneck U-Net and its potential as a reliable tool for radiologists in clinical practice. Our proposed model demonstrated rapid and effective cancer diagnosis capabilities, thus reducing diagnosis time. The model exhibited an impressive accuracy rate of 98.64% and a specificity score of 99.71%, outperforming both LSTM-ANN and GA Algorithms. This not only attests to the efficacy of our model but also underscores its potential in advancing diagnostic methodologies in clinical settings.

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Colon and Lung Cancer Classification of Histopathological Images Using Efficientnetb7

Shourie,

Anand,

Gupta

2023

2023 3rd Asian Conference on Innovation in Technology (ASIANCON)

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Classification of Colon and Lung Cancer Through Analysis of Histopathology Images Using Deep Learning Models

Cited by 5 publications

References 24 publications

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis

Bottleneck Feature-Based U-Net for Automated Detection and Segmentation of Gastrointestinal Tract Tumors from CT Scans

Colon and Lung Cancer Classification of Histopathological Images Using Efficientnetb7

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