Deep learning enhancement on mammogram images for breast cancer detection

Singla, Chaitanya; Sarangi, Pradeepta Kumar; Sahoo, Ashok Kumar; Singh, Pramod Kumar

doi:10.1016/j.matpr.2020.10.951

Cited by 29 publications

(11 citation statements)

References 9 publications

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“…Specificity (Singla et al, 2022) calculates the true negative values that are identified using a model. In this, most of the time model depicts the negative values using Equation ().

Specificity = \frac{T_{n}}{{normalT}_{normaln} + {normalF}_{p}}

…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A novel hybrid CNN methodology for automated leaf disease detection and classification

Kaur,

Mishra,

Goyal

et al. 2024

Expert Systems

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Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

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“…Specificity (Singla et al, 2022) calculates the true negative values that are identified using a model. In this, most of the time model depicts the negative values using Equation ().

Specificity = \frac{T_{n}}{{normalT}_{normaln} + {normalF}_{p}}

…”

Section: Experimental Results and Analysismentioning

confidence: 99%

A novel hybrid CNN methodology for automated leaf disease detection and classification

Kaur,

Mishra,

Goyal

et al. 2024

Expert Systems

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“…2D feature maps are included for better feature extraction. Singla C, et al [8], Mammogram screening plays an important screening for the detection of breast cancer. It has a very poor-quality image so it is very difficult for them to identify cancer from the given input.…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model

Abinaya¹,

Sivakumar²

2022

IJACSA

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Cancer is a major origin of mortality around the globe, responsible for roughly high morbidity and mortality in 2020, or almost one per six deaths. Cervical, lung, and breast are the most common types of cancers. Cervical is the fourth highest common in women worldwide. Cervical would then kill approximately 4,280 women. Infections that cause, such as human papillomavirus (HPV) and hepatitis, account for approximately 30% of cases in low-and lower-middle-income countries. Many cancers are curable if detected as early as possible. In this proposed work, developed the DA-Deep convnets model (Data augmentation with a deep, Convolutional Neural Network) for the detection of cervical cancer from biopsy images. Deep Convolutional Neural Network presents one of the most applied DL approaches in medical imaging. Today, enhancements in image analysis and processing, particularly medical imaging, have become a major factor in the improvement of various systems in areas such as medical prognosis, treatment, and diagnosis. Based on our proposed model we achieved 99.2% accuracy in detecting the input image has cancer or not.

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“…Table 1 shows the Results of comparing the study with some of the other studies that aimed to improve breast cancer diagnostic images using several techniques. They were compared in terms of samples, methods, results, and the contribution of each study [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

Alshamrani

Alqahtani

et al. 2022

Sensors

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In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

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Deep learning enhancement on mammogram images for breast cancer detection

Cited by 29 publications

References 9 publications

A novel hybrid CNN methodology for automated leaf disease detection and classification

A novel hybrid CNN methodology for automated leaf disease detection and classification

Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

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