Intelligent Diagnostic System Research Articles

Retraction Note: Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis

Retraction Note: Machine intelligent diagnostic system (MIDs): an instance of medical diagnosis of tuberculosis

Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.

Card fault diagnosis of the pressurized water reactor off-heap nuclear measurement system based on expert experience and convolutional neural network

The reactor nuclear measurement system is important in a nuclear power plant. Its main role is to measure the reactor's core power distribution using detectors and calibrate and provide data on the core fuel consumption. This study describes the lack of fault data and the lack of diagnostic methodology research in the overhauling process and fault diagnosis of the off-heap nuclear measurement system core card. This core card provides the detectors with the necessary working conditions. It also collects signals. In this study, we propose a methodology for the fault diagnosis of the card through circuit analysis, simulation of functional module division, fault data generation, and training of a convolutional neural network diagnostic model. The proposed methodology can transform the drawings into convenient diagnostic processes and algorithms based on expert experience. These drawings are difficult to use in actual overhauling conditions. The corresponding experimental equipment was designed for practical testing. The experimental results show that the accuracy of the obtained diagnostic model for classifying preset faults can reach 99.5%, indicating that this model can be applied in actual working conditions. The accuracy of the trained diagnostic model in classifying 13 kinds of faults in the training set during the actual test was tested. Results show that the accuracy rate is close to 100%. Moreover, the correction of the model using the real maintenance data in applying the actual maintenance conditions was also analyzed. The intelligent diagnostic system that centers on the fault diagnosis method investigated in this study has been applied in the pressurized water reactor off-heap nuclear measurement system digital transformation and upgrading project of Qinshan No. 2 Plant.

Infrastructure Diagnosed by Solar Power Supply in an Intelligent Diagnostic System in Five-Valued Logic

This article discusses the issue of diagnosing low-power solar power plants using the five-valued (5VL) state evaluation {4, 3, 2, 1, 0}. We address in depth how the 5VL diagnostics built upon 2VL, 3VL, and 4VL—two-valued diagnostics, three-valued logistics, and four-valued diagnostics. Logic (5VL) assigns five state values to the range of signal value changes, and these states are completely operational ({4}), incomplete ({3}), critical efficiency ({2}), and pre-fault efficiency ({1}). For the identical ranges of diagnostic signal values, all three of the applied state valence logics interpret failure as changes outside of their permitted ranges. Diagnostic procedures made use of an AI-based DIAG 2 system. This article’s goal is to provide a comprehensive overview of the DIAG 2 intelligent diagnostic system, including its architecture, algorithm, and inference rules. Diagnosis with the DIAG 2 system is based on a well-established technique for comparing diagnostic signal vectors with reference signal vectors. A differential vector metric is born out of this examination of vectors. The input cells of the neural network implement the challenge of signal analysis and comparison. It is then possible to classify the object components’ states in the neural network’s output cells. Based on the condition of the object’s constituent parts, this approach can signal whether those parts are working, broken, or urgently require replacement.

Development of an Artificial Intelligence Diagnostic System Using Linked Color Imaging for Barrett's Esophagus.

Background: Barrett's esophagus and esophageal adenocarcinoma cases are increasing as gastroesophageal reflux disease increases. Using artificial intelligence (AI) and linked color imaging (LCI), our aim was to establish a method of diagnosis for short-segment Barrett's esophagus (SSBE). Methods: We retrospectively selected 624 consecutive patients in total at our hospital, treated between May 2017 and March 2020, who experienced an esophagogastroduodenoscopy with white light imaging (WLI) and LCI. Images were randomly chosen as data for learning from WLI: 542 (SSBE+/- 348/194) of 696 (SSBE+/- 444/252); and LCI: 643 (SSBE+/- 446/197) of 805 (SSBE+/- 543/262). Using a Vision Transformer (Vit-B/16-384) to diagnose SSBE, we established two AI systems for WLI and LCI. Finally, 126 WLI (SSBE+/- 77/49) and 137 LCI (SSBE+/- 81/56) images were used for verification purposes. The accuracy of six endoscopists in making diagnoses was compared to that of AI. Results: Study participants were 68.2 ± 12.3 years, M/F 330/294, SSBE+/- 409/215. The accuracy/sensitivity/specificity (%) of AI were 84.1/89.6/75.5 for WLI and 90.5/90.1/91.1/for LCI, and those of experts and trainees were 88.6/88.7/88.4, 85.7/87.0/83.7 for WLI and 93.4/92.6/94.6, 84.7/88.1/79.8 for LCI, respectively. Conclusions: Using AI to diagnose SSBE was similar in accuracy to using a specialist. Our finding may aid the diagnosis of SSBE in the clinic.

AN EPILEPSY DIAGNOSIS METHOD BASED ON A TRANSFERRED ALEXNET MODEL AND EEG SIGNALS

Epilepsy is a neurological disorder, and its sudden seizures pose a serious threat to the quality of life of patients. Not only does this condition cause patients to potentially lose control and consciousness during seizures, leading to possible injuries or dangerous situations, but it also has a significant impact on their mental health, triggering issues such as anxiety and depression. An intelligent epilepsy diagnosis process based on electroencephalogram (EEG) signals offers notable advantages. First, it provides noninvasive brainwave signals that accurately monitor and record the patterns and characteristics of epileptic seizures, offering objective diagnostic criteria for doctors. Second, with the use of artificial intelligence and machine learning technologies, large amounts of EEG data can be efficiently analyzed, improving the speed and accuracy of diagnosis and providing timely and effective treatment plans for patients. Additionally, intelligent diagnostic systems can achieve real-time monitoring, promptly alert people to potential epileptic seizures, and provide a safer living environment for patients. In this context, this paper proposes an epilepsy diagnosis method based on a transferred AlexNet model and EEG signals. The main contributions of this paper are as follows. (1) A transfer learning mechanism is incorporated into the AlexNet model through the direct transfer of its neural network structure and the modification of some existing neural network structures, followed by collaborative training with the addition of a domain adaptation layer in the network. This introduced transfer mechanism can address small sample size issues. (2) The traditional AlexNet model suffers from redundant feature extraction, leading to slow training. This paper adds batch normalization (BN) layers after each convolutional layer in the AlexNet model to normalize the features extracted from the convolutional layers. This emphasizes the representation of the important features of EEG signals and enables the lower layers of the network to learn the features needed for EEG signal processing. (3) The transferred AlexNet model proposed in this paper is applied to extract the features of epilepsy EEG signals, and the extracted features are input into a support vector machine (SVM) classifier to obtain epilepsy diagnosis results. Comparative experiments show that the diagnostic method used in this paper yields superior results and shorter training times than those of the competing approaches.

CONCEPTS OF CREATING AN INTELLIGENT MEDICAL DIAGNOSTIC SYSTEM TO ASSIST IN THE WORK AND TRAINING OF DOCTORS BASED ON ARTIFICIAL INTELLIGENCE

Abstract. The article is devoted to the development of intelligent medical diagnostic systems using information systems and technologies. The article provides an overview of the current state and development of information systems, technologies and artificial intelligence in the medical field, analyzes existing intelligent diagnostic systems in medicine and cardiology in particular, and proves the need to create intelligent systems to help primary care physicians and cardiologists. The purpose of the publication was to investigate the current state of intelligent medical diagnostic systems, analyze the shortcomings of such systems, determine the feasibility of creating a new diagnostic system, and formulate principles and criteria for its operation. The scientific novelty of the article is a new approach for diagnosing and treating patients, as well as training medical professionals using an intelligent medical system based on artificial intelligence. Leveraging advanced AI algorithms, the system analyzes vast datasets encompassing patient records, medical literature, and real-time clinical data to provide accurate and timely diagnostic insights. The intelligent medical diagnostic system operates as a supportive tool, aiding doctors in the diagnostic process by offering refined suggestions, identifying potential anomalies, and recommending personalized treatment plans. By harnessing machine learning and deep learning techniques, the system continuously adapts and evolves, learning from each diagnostic scenario and refining its predictive accuracy over time. Through interactive simulations and case-based learning modules, aspiring and practicing doctors can engage in immersive, realistic scenarios, honing their diagnostic skills and expanding their knowledge base. The disadvantages of this article include only a theoretical approach to the formation of concepts and tasks for an intelligent medical system, and a review of existing systems.

Development and research of an intelligent diagnostic system for equipment of electric power complexes

Intelligent systems represent a new direction in the electrical power industry, and training students in this area requires appropriate updating of curricula and laboratory equipment. In this regard, it is necessary to create educational and research complexes in special disciplines to train specialists in intelligent electric power systems. This article presents an educational and research laboratory complex with elements of artificial intelligence for diagnosing the technical condition of equipment in electric power complexes. A free version of the IDE was used as an integrated development environment, which provides the basic functions and tools necessary for developing and debugging Python projects. The software part of the complex has been developed, including a digital twin of the laboratory installation, an executive part and a neural network model.

An Intelligent Diagnostic System to Analyze Early-Stage Chronic Kidney Disease for Clinical Application

Chronic kidney disease (CKD) is a progressive condition characterized by the gradual deterioration of kidney functions, potentially leading to kidney failure if not promptly diagnosed and treated. Machine learning (ML) algorithms have shown significant promise in disease diagnosis, but in healthcare, clinical data pose challenges: missing values, noisy inputs, and redundant features, affecting early-stage CKD prediction. Thus, this study presents a novel, fully automated machine learning approach to tackle these complexities by incorporating feature selection (FS) and feature space reduction (FSR) techniques, leading to a substantial enhancement of the model’s performance. A data balancing technique is also employed during preprocessing to address data imbalance issue that is commonly encountered in clinical contexts. Finally, for reliable CKD classification, an ensemble characteristics-based classifier is encouraged. The effectiveness of our approach is rigorously validated and assessed on multiple datasets, and the clinical relevancy of the strategy is evaluated on the real-world therapeutic data collected from Bangladeshi patients. The study establishes the dominance of adaptive boosting, logistic regression, and passive aggressive ML classifiers with 96.48% accuracy in forecasting unseen therapeutic CKD data, particularly in early-stage cases. Furthermore, the effectiveness of the FSR technique in reducing the prediction time significantly is revealed. The outstanding performance of the proposed model demonstrates its effectiveness in addressing the complexity of healthcare CKD data by incorporating the FS and FSR techniques. This highlights its potential as a promising computer-aided diagnosis tool for doctors, enabling early interventions and improving patient outcomes.

Intelligent Diagnostic System Development based on Artificial Intelligence Technology

Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It is necessary to ask the user if they have any other symptoms through dialogue to form a diagnostic conclusion. Existing research mainly adopts reinforcement learning methods, which gradually learn the dialogue process between traditional Chinese medicine students and patients in real medical scenarios, and obtain strategies for symptom inquiry and disease diagnosis. Despite the advantages of reinforcement learning in dealing with temporal decision problems, the diagnostic accuracy is still low and data dependency is strong. In this article, a medical dialogue robot architecture based on medical dialogue diagnosis technology, medical knowledge graph technology, and "inference machine" technology is proposed to build an intelligent diagnosis architecture. Secondly, in terms of algorithm, this article proposes a disease diagnosis algorithm based on Naive Bayes Classification and a symptom screening algorithm based on symptom set differences for symptom query process, This algorithm increases the interpretability of diagnostic results by simulating the questioning and diagnostic process of doctors, and combines it with the medical dialogue robot architecture to achieve intelligent diagnosis throughout the entire process.

Physics-guided generative adversarial network for probabilistic structural system identification

Structural system identification (ID) is an important tool for many structural and infrastructure applications, such as structural health monitoring and structural model updating. A novel physics-guided generative adversarial network (PG-GAN) is proposed in this study for probabilistic structural parameter ID. The PG-GAN leverages the powerful distribution learning ability of GANs while also enabling physical awareness by incorporating additional generators for physical parameters and developing a physics-based loss function that links the data generator with the parameter generators through existing physics knowledge to guide the training of generative models. Two experiments are performed to validate the effectiveness of the PG-GAN for structural parameter ID and demonstrate its application in structural damage detection. The PG-GAN is shown to successfully capture the uncertainty inherent in structural parameter estimation and accurately locate and quantify structural damages in terms of probabilistic distributions of the damage extent. The main contributions of this study are threefold: (1) compared with traditional methods for structural parameter ID, the proposed PG-GAN provides an intelligent framework for probabilistic identification of structural parameters without the need for any assumption on the data distribution and does not require an initial guess of parameters; (2) unlike traditional data-driven GANs, the proposed PG-GAN provides a linkage between GAN and physics knowledge to enables physical awareness in generative model training; and (3) the proposed PG-GAN can serve as an intelligent diagnostic system to better inform the decision-making on structural health management as it can quantify the uncertainty in the structural damage extent.

Real Coded Binary Artificial Bee Colony (RC-BABC) Based Feature Selection and Relieff Based Feature Extraction Techniques for Heart Disease Prediction

Diagnosing heart disease is really a challenging task for which several intelligent diagnostic systems were developed for enhancing the performance of diagnosing heart disease. However, in these systems, low accuracy of predicting heart disease is still a challenging task. To provide better accuracy in predicting heart risks, a novel feature selection approach is proposed which employs Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm with adaptive size for feature elimination. This method has the advantages of reducing algorithmic computational time, improving prediction accuracy, enhanced data quality, and saves resources in successive data collection phases. Once the features are selected, the important feature extraction phase uses ReliefF based feature extraction method to extract the features from the heart disease data set. The scores of features are computed by estimating a comparison of feature values and class values neighbor samples. The proposed Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm is compared with three well known methods namely an artificial neural network (ANN), K-means clustering approach and Classification and Regression Algorithm (C&RT) with measures like accuracy, precision, recall and F1-score. The proposed method achieved 96.77% of accuracy,98.8% of recall, 97.8% of precision and 98.34% of F1-score.

Value of CT examination combined with serum tumor markers assisted with artificial intelligence diagnostic system in the diagnosis of benign and malignant pulmonary nodules

Value of CT examination combined with serum tumor markers assisted with artificial intelligence diagnostic system in the diagnosis of benign and malignant pulmonary nodules

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data

BackgroundLaryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.MethodsAll 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS.ResultsLPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965–0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications.ConclusionsLPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance.

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study

BackgroundThe introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML).MethodsThis study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots.ResultsThe random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively.ConclusionsThe PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study.Trial registrationChinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

Application of optical analysis methods for non-invasive monitoring of blood oxygen saturation level

An intelligent optical system for medical express diagnostics has been developed and tested. A method for visualizing the oxygen status of biological tissues in the form of "digital images" describing the general functional state of the human body is demonstrated. It has been shown that the method of principal components and hierarchical clustering can be used in combination with optical methods for detecting hemoglobin forms in biological tissues to perform non-invasive monitoring and express diagnostics of the oxygen status of the human body. The results obtained show that it is possible to stratify the subjects into risk groups based on optical sensor readings. In comparison with pulse oximetry, the use of which is common for determining the oxygen saturation level of blood, the described method can be employed to estimate peripheral oxygen saturation, and thus thrombosis and ischemia of the extremities can be detected in time.

Intelligent diagnostic system for Cryptococcus: Switch-controllable nanocatcher and CNN-based artificial intelligence

Each year, one million Cryptococcal infections occur among people living with HIV/AIDS, resulting in nearly 625,000 fatalities due to low efficiency of clinical diagnostic procedures and misdiagnosis caused by faulty subjective judgment of manually enumerated imaging-based infection assays. In order to improve the diagnostic efficiency of non-immunodeficient patients infected with Cryptococcus, the present study developed an intelligent diagnostic system combined with a switch-controllable nanocatcher (MNP@PNIPAMAA-CAS) with high capture performance for Cryptococcus and convolutional neural network (CNN)-based artificial intelligence (AI), which was utilized to analyze the collected pathological images for subsequent diagnosis. This system benefitted from the high adsorption efficiency of the MNP@PNIPAMAA-CAS and the objective data processing ability of AI. The system’s detection limit was 1–4 cells /mL, the specificity was 97%, and the detection time was only 6 min. The intelligent diagnostic system will provide significant benefit to the diagnosis of clinical fungi due to its high sensitivity, high specificity, and superfast detection speed.

Deep learning based classification of multi-label chest X-ray images via dual-weighted metric loss

—Thoracic disease, like many other diseases, can lead to complications. Existing multi-label medical image learning problems typically include rich pathological information, such as images, attributes, and labels, which are crucial for supplementary clinical diagnosis. However, the majority of contemporary efforts exclusively focus on regression from input to binary labels, ignoring the relationship between visual features and semantic vectors of labels. In addition, there is an imbalance in data amount between diseases, which frequently causes intelligent diagnostic systems to make erroneous disease predictions. Therefore, we aim to improve the accuracy of the multi-label classification of chest X-ray images. Chest X-ray14 pictures were utilized as the multi-label dataset for the experiments in this study. By fine-tuning the ConvNeXt network, we got visual vectors, which we combined with semantic vectors encoded by BioBert to map the two different forms of features into a common metric space and made semantic vectors the prototype of each class in metric space. The metric relationship between images and labels is then considered from the image level and disease category level, respectively, and a new dual-weighted metric loss function is proposed. Finally, the average AUC score achieved in the experiment reached 0.826, and our model outperformed the comparison models.

Integrated IoT-based Healthcare System for the Early Detection of Breast Cancer Using Intelligent Diagnostic System

Breast cancer represents one of the leading cancer-related diseases worldwide, affecting mostly women after puberty. Even though the illness is fatal and kills thousands of people each year, it is mostly curative if found quickly. As a result, prompt and precise detection methods are critical to patient survival. Previously, doctors used manual detection systems for this objective. However, such techniques have been slow and frequently dependent on the physician's expertise. As technology advanced, these primitive methodologies were supplemented by computer-aided detection (CAD) algorithms. Deep learning is extremely common because of the massive development in large data, the Internet of Things (IoT), linked devices, and high-performance computers using GPUs and TPUs. The Internet of Things (IoT) has advanced recently, and the healthcare industry is benefiting from this growth. Sensors that gather data for required analysis are crucial components utilized in the Internet of Things. Physicians and medical staff will be able to carry out their tasks with ease and intelligence thanks to the Internet of Things. The proposed research focus on integrating Alexnet and ResNet101 for accurate prediction of Breast malignancy from mammogram data. This methodology will target the features more precisely than any other combination of the pre-trained model. Finally, to resolve the computational burden issue, the feature reduction ReliefF methodology is used. To demonstrate the proposed method, an online publicly released set of data of 750 BU images is used. For training and testing the models, the set of data has been further split into 80 and 20% ratios. Following extensive testing and analysis, it was discovered that the DenseNet-201 and MobileNet-v2 trained SVMs to have an accuracy of 98.39 percent for the original and augmented Mammo images online datasets, respectively. This research discovered that the proposed approach is efficient and simple to implement to assist radiographers and physicians in diagnosing breast cancer in females.