Development Of Computer-aided Diagnosis Research Articles

NA-segformer: A multi-level transformer model based on neighborhood attention for colonoscopic polyp segmentation

In various countries worldwide, the incidence of colon cancer-related deaths has been on the rise in recent years. Early detection of symptoms and identification of intestinal polyps are crucial for improving the cure rate of colon cancer patients. Automated computer-aided diagnosis (CAD) has emerged as a solution to the low efficiency of traditional methods relying on manual diagnosis by physicians. Deep learning is the latest direction of CAD development and has shown promise for colonoscopic polyp segmentation. In this paper, we present a multi-level encoder-decoder architecture for polyp segmentation based on the Transformer architecture, termed NA-SegFormer. To improve the performance of existing Transformer-based segmentation algorithms for edge segmentation on colon polyps, we propose a patch merging module with a neighbor attention mechanism based on overlap patch merging. Since colon tract polyps vary greatly in size and different datasets have different sample sizes, we used a unified focal loss to solve the problem of category imbalance in colon tract polyp data. To assess the effectiveness of our proposed method, we utilized video capsule endoscopy and typical colonoscopy polyp datasets, as well as a dataset containing surgical equipment. On the datasets Kvasir-SEG, Kvasir-Instrument and KvasirCapsule-SEG, the Dice score of our proposed model reached 94.30%, 94.59% and 82.73%, with an accuracy of 98.26%, 99.02% and 81.84% respectively. The proposed method achieved inference speed with an Frame-per-second (FPS) of 125.01. The results demonstrated that our suggested model effectively segmented polyps better than several well-known and latest models. In addition, the proposed method has advantages in trade-off between inference speed and accuracy, and it will be of great significance to real-time colonoscopic polyp segmentation. The code is available at https://github.com/promisedong/NAFormer.

Opportunities and challenges of computer aided diagnosis in new millennium: A bibliometric analysis from 2000 to 2023.

After entering the new millennium, computer-aided diagnosis (CAD) is rapidly developing as an emerging technology worldwide. Expanding the spectrum of CAD-related diseases is a possible future research trend. Nevertheless, bibliometric studies in this area have not yet been reported. This study aimed to explore the hotspots and frontiers of research on CAD from 2000 to 2023, which may provide a reference for researchers in this field. In this paper, we use bibliometrics to analyze CAD-related literature in the Web of Science database between 2000 and 2023. The scientometric softwares VOSviewer and CiteSpace were used to visually analyze the countries, institutions, authors, journals, references and keywords involved in the literature. Keywords burst analysis were utilized to further explore the current state and development trends of research on CAD. A total of 13,970 publications were included in this study, with a noticeably rising annual publication trend. China and the United States are major contributors to the publication, with the United States being the dominant position in CAD research. The American research institutions, lead by the University of Chicago, are pioneers of CAD. Acharya UR, Zheng B and Chan HP are the most prolific authors. Institute of Electrical and Electronics Engineers Transactions on Medical Imaging focuses on CAD and publishes the most articles. New computer technologies related to CAD are in the forefront of attention. Currently, CAD is used extensively in breast diseases, pulmonary diseases and brain diseases. Expanding the spectrum of CAD-related diseases is a possible future research trend. How to overcome the lack of large sample datasets and establish a universally accepted standard for the evaluation of CAD system performance are urgent issues for CAD development and validation. In conclusion, this paper provides valuable information on the current state of CAD research and future developments.

Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images

PET/CT can scan low-dose computed tomography (LDCT) images with morphological information and PET images with functional information. Because the whole body is targeted for imaging, PET/CT examinations are important in cancer diagnosis. However, the several images obtained by PET/CT place a heavy burden on radiologists during diagnosis. Thus, the development of computer-aided diagnosis (CAD) and technologies assisting in diagnosis has been requested. However, because FDG accumulation in PET images differs for each organ, recognizing organ regions is essential for developing lesion detection and analysis algorithms for PET/CT images. Therefore, we developed a method for automatically extracting organ regions from PET/CT images using U-Net or DenseUNet, which are deep-learning-based segmentation networks. The proposed method is a hybrid approach combining morphological and functional information obtained from LDCT and PET images. Moreover, pre-training using ImageNet and RadImageNet was performed and compared. The best extraction accuracy was obtained by pre-training ImageNet with Dice indices of 94.1, 93.9, 91.3, and 75.1% for the liver, kidney, spleen, and pancreas, respectively. This method obtained better extraction accuracy for low-quality PET/CT images than did existing studies on PET/CT images and was comparable to existing studies on diagnostic contrast-enhanced CT images using the hybrid method and pre-training.

CP-AnemiC: A ​conjunctival pallor dataset and benchmark for anemia detection in children

Anemia is a universal public health issue, which occurs as the result of a reduction in red blood cells. This disease is common among children in Africa and other developing countries. If not treated early, children may suffer long-term consequences such as impairment in social, emotional, and cognitive functioning. Early detection of anemia in children is highly desirable for effective treatment measures. While there has been research into the development of computer-aided diagnosis (CAD) systems for anemia diagnosis, a significant proportion of these studies encountered limitations when working with limited datasets.To overcome the existing issues, this paper proposes a large dataset, named CP-AnemiC, comprising 710 individuals (range of age, 6–59 months), gathered from several hospitals in Ghana. The conjunctiva image-based dataset is supported with Hb levels (g/dL) annotations for accurate diagnosis of anemia. A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels (g/dL) based on the conjunctival pallor images. This paper conducts a comprehensive experiment on the CP-AnemiC dataset. The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels (g/dL) estimation.

Multi-Stage Ensemble-Based System for Glaucomatous Optic Neuropathy Diagnosis in Fundus Images

Recent developments in Computer-aided Diagnosis (CAD) systems as a countermeasure to the increasing number of untreated cases of eye diseases related to visual impairment (such as diabetic retinopathy or age-related macular degeneration) have the potential to yield in low-to-mid income countries a comfortable and accessible alternative to obtaining a general ophthalmological study necessary for follow-up medical attention. In this work, a multi-stage ensemble-based system for the diagnosis of glaucomatous optic neuropathy (GON) is proposed. GON diagnosis is based on a binary classification procedure working in conjunction with a multi-stage block based on image preprocessing and feature extraction. Our preliminary data show similar results compared to current studies considering metrics such as Accuracy, Sensitivity, Specificity, AUC (AUROC), F1score, and the use of Matthews Correlation Coefficient (MCC) as an additional performance metric is proposed.

An open-access breast lesion ultrasound image database‏: Applicable in artificial intelligence studies

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm.

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN's proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%.

A Markov random field approach for CT image lung classification using image processing

The performance of computed tomography lung classification using image processing and Markov Random Field was investigated in this study. For lung classification, the process must first be going through lung segmentation process. Lung segmentation is important as an initial process before lung cancer segmentation and analysis. Image processing was employed to the input image. We propose multilevel thresholding and Markov Random Field to improve the segmentation process. Three setting for Markov Random Field was used for segmentation process that is Iterated Condition Mode, Metropolis algorithm and Gibbs sampler. Then, the process of classifying lung will proceed. The output from the experiments were analysed and compared to get the best performance. The results revealed that for CT image lung classification, Markov Random Field using Metropolis algorithm gives the best results. In view of the result obtained, the average accuracy is 94.75% while the average sensitivity and specificity are 76.34% and 99.80%. The output from this study can be implemented in lung cancer analysis research and computer aided diagnosis development.

Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT

ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by harmful levels of inattention, disorganization, and/or hyperactivity-impulsivity. In childhood, these symptoms often overlap with those of other disorders, and they tend to persist into adulthood, interfering with relationships and academic and work life. Diagnosis, traditionally made by assessing the patient, i.e., testing and listening to relatives and teachers, has already been aided by neuroimaging. However, the visual analysis of such images to make a psychiatric diagnosis is a complex and sometimes time-consuming task. For this reason, computer-aided diagnostic tools have increasingly evolved that, when combined with machine learning (ML) techniques, can accelerate, facilitate, and maximize the accuracy of diagnoses. Nevertheless, research evaluating ML models for classifying ADHD considering severity using images of the brain SPECT (Single Photon Emission Computed Tomography) is still very sparse. For this reason, this article aims to evaluate the performance of the ML methods: k-NN (k-Nearest Neighbors), Naive Bayes, Decision Tree, MLP (Multilayer Perceptron) and SVM (Support Vector Machine) in the classification of ADHD. The main goal of this analysis is to check whether the subjects have the disorder or not, and to classify the severity of those who have it using SPECT images. A database was created from SPECT images and diagnostic reports. After pre-processing these data, the best hyperparameters for the ML methods were searched, trained/tested and finally statistically compared. The best results were obtained with SVM and k-NN, with 98% accuracy. Although ADHD diagnosis by neuroimaging is not yet a standard clinical procedure, we argue that this study can contribute to ADHD diagnosis research and support methods for the development of CAD (computer-aided diagnosis) systems.

A Reproducible Deep-Learning-Based Computer-Aided Diagnosis Tool for Frontotemporal Dementia Using MONAI and Clinica Frameworks.

Despite Artificial Intelligence (AI) being a leading technology in biomedical research, real-life implementation of AI-based Computer-Aided Diagnosis (CAD) tools into the clinical setting is still remote due to unstandardized practices during development. However, few or no attempts have been made to propose a reproducible CAD development workflow for 3D MRI data. In this paper, we present the development of an easily reproducible and reliable CAD tool using the Clinica and MONAI frameworks that were developed to introduce standardized practices in medical imaging. A Deep Learning (DL) algorithm was trained to detect frontotemporal dementia (FTD) on data from the NIFD database to ensure reproducibility. The DL model yielded 0.80 accuracy (95% confidence intervals: 0.64, 0.91), 1 sensitivity, 0.6 specificity, 0.83 F1-score, and 0.86 AUC, achieving a comparable performance with other FTD classification approaches. Explainable AI methods were applied to understand AI behavior and to identify regions of the images where the DL model misbehaves. Attention maps highlighted that its decision was driven by hallmarking brain areas for FTD and helped us to understand how to improve FTD detection. The proposed standardized methodology could be useful for benchmark comparison in FTD classification. AI-based CAD tools should be developed with the goal of standardizing pipelines, as varying pre-processing and training methods, along with the absence of model behavior explanations, negatively impact regulators’ attitudes towards CAD. The adoption of common best practices for neuroimaging data analysis is a step toward fast evaluation of efficacy and safety of CAD and may accelerate the adoption of AI products in the healthcare system.

Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Due to the poor contrast, noise and artifacts which results in difficulty for radiologists to diagnose, Computer-Aided Diagnosis (CAD) systems are hence developed. The extraction of breast region is a fundamental and crucial preparation step for further development of CAD systems.OBJECTIVE: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed.METHODS: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using CLAHE and semantic segmentation using Deeplab v3 model.RESULTS: The method is trained and evaluated on mini-MIAS dataset. It has also been evaluated on INbreast dataset. The results outperform those generated by other recent researches and are indicative of the capacity of the model to retain its accuracy and runtime advantage across different databases with different image resolutions.CONCLUSIONS: The proposed method shows state-of-the-art performance at extracting breast region from mammographic images. Wide range of evaluation on two commonly used mammography datasets proves the ability and adaptability of the method.

A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted.

Automatic Carpal Site Detection Method for Evaluation of Rheumatoid Arthritis Using Deep Learning

Approximately 600,000 to 1,000,000 patients are diagnosed with rheumatoid arthritis (RA) in Japan. To provide appropriate treatment, it is necessary to accurately measure the progression of RA by diagnosing the disease several times a year. The modified total sharp score (mTSS) calculated from hand X-ray images is a standard diagnostic method for RA progression. However, this diagnostic method is time-consuming as the scores are rated at as many as 16 points per hand. Accordingly, in order to shorten the diagnosis time of RA patients and improve the quality of diagnosis, the development of computer-aided diagnosis (CAD) systems is expected. We have previously proposed a CAD system that can detect finger joint positions using a support vector machine and can estimate the mTSS using ridge regression. In this study, we propose a fully automatic detection method of RA score evaluation points in the carpal site from simple hand X-ray images using deep learning. The proposed method first segments the carpal site using deep learning. Next, the RA evaluation points are automatically determined from each segment based on prior knowledge. Experimental results on X-ray images of the hands of 140 patients with RA showed that the mTSS evaluation point at the carpal site could be detected with an average error of 25 pixels. This study enables the automatic detection of RA score evaluation points in the carpal site. In the diagnosis of RA, the time required for diagnosis can be reduced by automating the determination of diagnostic points by physician.

Breast Tumour Classification Using Ultrasound Elastography with Machine Learning: A Systematic Scoping Review.

Simple SummaryBreast cancer is one of the most common cancers among women globally. Early and accurate screening of breast tumours can improve survival. Ultrasound elastography is a non-invasive and non-ionizing imaging approach to characterize lesions for breast cancer screening, while machine learning techniques could improve the accuracy and reliability of computer-aided diagnosis. This review focuses on the state-of-the-art development and application of the machine learning model in breast tumour classification.Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ≥80%, while only half of them attained acceptable specificity ≥95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.

Segmentation information with attention integration for classification of breast tumor in ultrasound image

Breast cancer is one of the most common forms of cancer among women worldwide. The development of computer-aided diagnosis (CAD) technology based on ultrasound imaging to promote the diagnosis of breast lesions has attracted the attention of researchers and deep learning is a popular and effective method. However, most of the deep learning based CAD methods neglect the relationship between two vision tasks tumor region segmentation and classification. In this paper, taking into account some prior knowledges of medicine, we propose a novel segmentation-to-classification scheme by adding the segmentation-based attention (SBA) information to the deep convolution network (DCNN) for breast tumors classification. A segmentation network is trained to generate tumor segmentation enhancement images. Then two parallel networks extract features for the original images and segmentation enhanced images and one channel attention based feature aggregation network is to automatically integrate the features extracted from two feature networks to improve the performance of recognizing malignant tumors in the breast ultrasound images. To validate our method, experiments have been conducted on breast ultrasound datasets. The classification results of our method have been compared with those obtained by eleven existing approaches. The experimental results show that the proposed method achieves the highest Accuracy (90.78%), Sensitivity (91.18%), Specificity (90.44%), F1-score (91.46%), and AUC (0.9549).

Incorporating clinical knowledge with constrained classifier chain into a multimodal deep network for melanoma detection

In recent years, vast developments in Computer-Aided Diagnosis (CAD) for skin diseases have generated much interest from clinicians and other eventual end-users of this technology. Introducing clinical domain knowledge to these machine learning strategies can help dispel the black box nature of these tools, strengthening clinician trust. Clinical domain knowledge also provides new information channels which can improve CAD diagnostic performance. In this paper, we propose a novel framework for malignant melanoma (MM) detection by fusing clinical images and dermoscopic images. The proposed method combines a multi-labeled deep feature extractor and clinically constrained classifier chain (CC). This allows the 7-point checklist, a clinician diagnostic algorithm, to be included in the decision level while maintaining the clinical importance of the major and minor criteria in the checklist. Our proposed framework achieved an average accuracy of 81.3% for detecting all criteria and melanoma when testing on a publicly available 7-point checklist dataset. This is the highest reported results, outperforming state-of-the-art methods in the literature by 6.4% or more. Analyses also show that the proposed system surpasses the single modality system of using either clinical images or dermoscopic images alone and the systems without adopting the approach of multi-label and clinically constrained classifier chain. Our carefully designed system demonstrates a substantial improvement over melanoma detection. By keeping the familiar major and minor criteria of the 7-point checklist and their corresponding weights, the proposed system may be more accepted by physicians as a human-interpretable CAD tool for automated melanoma detection.

Intelligent Computer-Aided Prostate Cancer Diagnosis Systems: State-of-the-Art and Future Directions

Prostate Cancer (PCa) is one of the common cancers among men in the world. About 16.67% of men will be affected by PCa in their life. Due to the integration of magnetic resonance imaging in the current clinical procedure for detecting prostate cancer and the apparent success of imaging techniques in the estimation of PCa volume in the gland, we provide a more detailed review of methodologies that use specific parameters for prostate tissue representation. After collecting over 200 researches on image-based systems for diagnosing prostate cancer, in this paper, we provide a detailed review of existing computer-aided diagnosis (CAD) methods and approaches to identify prostate cancer from images generated using Near-Infrared (NIR), Mid-Infrared (MIR), and Magnetic Resonance Imaging (MRI) techniques. Furthermore, we introduce two research methodologies to build intelligent CAD systems. The first methodology applies a fuzzy integral method to maintain the diversity and capacity of different classifiers aggregation to detect PCa tumor from NIR and MIR images. The second methodology investigates a typical workflow for developing an automated prostate cancer diagnosis using MRI images. Essentially, CAD development remains a helpful tool of radiology for diagnosing prostate cancer disease. Nonetheless, a complete implementation of effective and intelligent methods is still required for the PCa-diagnostic system. While some CAD applications work well, some limitations need to be solved for automated clinical PCa diagnostic. It is anticipated that more advances should be made in computational image analysis and computer-assisted approaches to satisfy clinical needs shortly in the coming years.

Detection of Lung Nodules on CT Images based on the Convolutional Neural Network with Attention Mechanism

The development of Computer-aided diagnosis (CAD) systems for automatic lung nodule detection through thoracic computed tomography (CT) scans has been an active area of research in recent years. Lung Nodule Analysis 2016 (LUNA16 challenge) encourages researchers to suggest a variety of successful nodule detection algorithms based on two key stages (1) candidates detection, (2) false-positive reduction. In the scope of this paper, a new convolutional neural network (CNN) architecture is proposed to efficiently solve the second challenge of LUNA16. Specifically, we find that typical CNN models pay little attention to the characteristics of input data, in order to address this constraint, we apply the attention-mechanism: propose a technique to attach Squeeze and Excitation-Block (SE-Block) after each convolution layer of CNN to emphasize important feature maps related to the characteristics of the input image - forming Attention sub-Convnet. The new CNN architecture is suggested by connecting the Attention sub-Convnets. In addition, we also analyze the selection of triplet loss or softmax loss functions to boost the rating performance of the proposed CNN. From the study, this is agreed to select softmax loss during the CNN training phase and triplet loss for the testing phase. Our suggested CNN is used to minimize the number of redundant candidates in order to improve the efficiency of false-positive reduction with the LUNA database. The results obtained in comparison to the previous models indicate the feasibility of the proposed model.

Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps.

.Significance: Colorectal cancer incidence has decreased largely due to detection and removal of polyps. Computer-aided diagnosis development may improve on polyp detection and discrimination.Aim: To advance detection and discrimination using currently available commercial colonoscopy systems, we developed a deep neural network (DNN) separating the color channels from images acquired under narrow-band imaging (NBI) and white-light endoscopy (WLE).Approach: Images of normal colon mucosa and polyps from colonoscopies were studied. Each color image was extracted based on the color channel: red/green/blue. A multilayer DNN was trained using one-channel, two-channel, and full-color images. The trained DNN was then tested for performance in detection of polyps.Results: The DNN performed better using full-colored NBI over WLE images in the detection of polyps. Furthermore, the DNN performed better using the two-channel red + green images when compared to full-color WLE images.Conclusions: The separation of color channels from full-color NBI and WLE images taken from commercially available colonoscopes may improve the ability of the DNN to detect and discriminate polyps. Further studies are needed to better determine the color channels and combination of channels to include and exclude in DNN development for clinical use.

An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine

Over the past years, the surge in the necessity for early detection and diagnosis of breast cancer has resulted in many innovative research directions. According to the World Health Organization, an early and accurate detection of breast cancer successfully leads to a correct decision towards its treatment. Development of computer-aided diagnosis (CAD) system is considered to be a major stead in current research practice to abet medical practitioners in decision-making. This paper proposes an improved CAD framework to correctly classify the digital mammograms into normal or abnormal, and further, benign or malignant. The proposed scheme employs a block-based discrete wavelet packet transform (BDWPT) to extract the features, namely, the Shannon entropy, Tsallis entropy, Renyi entropy, and energy. Then, principal component analysis (PCA) technique is utilized to extract the discriminating features from the original feature vector. Subsequently, an optimized wrapper-based kernel extreme learning machine (KELM) using a weighted chaotic salp swarm algorithm (WC-SSA) is proposed as classifier to classify the digital mammograms. Since the efficacy of KELM algorithm depends on its two important parameters, namely, the penalty parameter and the kernel parameter, the prime objective of the proposed work is to obtain the optimized value of the aforementioned parameters as well as to select the most relevant features from the reduced feature vector simultaneously.The proposed scheme is evaluated on three publicly available standard datasets, namely, MIAS, DDSM, and BCDR to validate the efficacy of the proposed BDWPT+PCA+WC-SSA-KELM scheme. The performance of the proposed model is evaluated in terms of different metrics, namely, classification accuracy, sensitivity, specificity, area under curve (AUC), Matthew’s correlation coefficient (MCC), and F-measure via a 5 × 5 stratified cross-validation approach. From the experimental results and their analysis, it is observed that for the normal–abnormal category, the proposed technique results in an accuracy of 99.62% and 99.92% for MIAS and DDSM, respectively, whereas in the case of benign–malignant classification, the proposed method yields an accuracy of 99.28%, 99.63%, 99.60% for MIAS, DDSM, and BCDR datasets, respectively. Further, it is also observed that the proposed WC-SSA-KELM scheme exhibits superior performance as compared to that of its counterparts. Additionally, two well-known statistical analyses, namely, ANOVA and Friedman tests are performed to demonstrate that the performance of the proposed scheme is significantly better than that of the other existing schemes.