Early detection of breast malignancy using wavelet features and optimized classifier

Melekoodappattu, Jayesh George; Kadan, Anoop Balakrishnan; Anoop, V

doi:10.1002/ima.22537

Cited by 8 publications

(11 citation statements)

References 40 publications

(32 reference statements)

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“…The system’s effectiveness is evaluated using the prescribed performance metrics, and the results are shown in Table 3 . The introduced system efficiency is compared with deep convolution with a fully optimized framework (DC-FOF) [ 16 ], hybrid ensemble classifiers (HEC) [ 18 ], optimized radial basis function neural networks (RBFNN) [ 19 ], and multi-encoder net framework (MENF) [ 21 ]. The existing algorithm works effectively while recognizing the tumors.…”

Section: Resultsmentioning

confidence: 99%

“…Several classifiers [ 14 ], such as fuzzy clustering means (FCM), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Expectation-Maximization (EM), and knowledge-based techniques are introduced to recognize brain tumors [ 15 , 16 ]. However, it is difficult to identify the exact tumor region and hidden edge details with minimum computational complexity [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

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Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

Kurdi

Ali

Jaber

et al. 2023

JPM

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The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

Kurdi

Ali

Jaber

et al. 2023

JPM

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“…The CNN model is made up of nine layers in total. This model employs several batch normalization layers 27 to speed up the classification process and reduce overfitting. The CNN models are trained several times to discover the optimal system parameters in order to get the best performance in image recognition.…”

Section: Discussionmentioning

confidence: 99%

“…In general, the CNN model consists of nine layers. To accelerate the classification process and minimize the overfitting problem, this model uses multiple batch normalization layers 27 . To achieve the highest performance in image recognition, the CNN models are trained multiple times to find the right system parameters.…”

Section: Discussionmentioning

confidence: 99%

Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features

Melekoodappattu¹,

Dhas²,

Kumar³

et al. 2021

Int J Imaging Syst Tech

Self Cite

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Breast cancer is detected by identifying malignancy on breast tissue. Emerging technologies in medical image processing are used to interpret histopathology images. For analyzing medical imaging and pathological data, modified deep neural networks are being used. Automatic detection of malignancy is usually achieved in deep learning by capturing features from a convolutional neural network (CNN) and then categorizing them using a fully connected network. A framework to automatically diagnose malignancy using an ensemble approach, including CNN and extraction of image texture features, is implemented in this research. In the CNN phase, the nine‐layer modified CNN is used to classify images. Texture features are derived and their dimension is minimized using maximum variance unfolding to enhance the efficiency of classification in the extraction‐based phase. The results of each phase were then merged to obtain the final decision. The testing specificity and accuracy of our ensemble method on MIAS repository are 98.9% and 99%, respectively and for the DDSM repository are 98.3% and 98.1%. The ensemble approach increases the measurement metrics compared to each phase separately, as per the experimental findings.

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“…Regarding breast cancer, as a significant health issue among women and early detection makes treatment easier and more effective, medical imaging has been used in a variety of ways to investigate breast cancer. Moreover, Melekoodappattu et al [142] developed a computer-aided diagnostic (CAD) system to detect breast cancer by interpreting mammograms and identifying tumours with 99.33 % accuracy. On the other hand, Kim et al [143] affirmed that patients suffering from breast cancer are less likely to become depressed if they have a lot of support from their families and the more family support they receive, the less depressed they will be.…”

Section: Cancermentioning

confidence: 99%

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

Alqahtani¹,

Janbi²,

Sharaf³

et al. 2022

Preprint

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Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

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Early detection of breast malignancy using wavelet features and optimized classifier

Cited by 8 publications

References 40 publications

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

Malignancy detection on mammograms by integrating modified convolutional neural network classifier and texture features

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

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