asim ali khan

Scopus Publications

A comprehensive machine learning framework with particle swarm optimization for improved polycystic ovary syndrome (PCOS) diagnosis
Ankur Kumar, Jaspreet Singh, and Asim Ali Khan
IOP Publishing
Abstract Polycystic Ovary Syndrome (PCOS) is a hormonal disorder primarily affecting women of reproductive age, characterized by irregular menstrual cycles, elevated male hormones, and ovarian cysts. Early detection and treatment are crucial to prevent long-term complications. This research utilizes clinical data from Kaggle to develop a non-invasive PCOS diagnostic system. The authors conducted comprehensive data preprocessing, feature engineering, and exploratory data analysis (EDA). The refined dataset was incorporated into various default machine learning (ML) algorithms, including LR, LDA, GNB, SVM, XGB, DT, AB, RF, and KNN, for PCOS classification with varying train test ratios 70:30 to 80:20. To further enhance the model’s performance, the authors hybridized all the ML models with Particle Swarm Optimization (PSO). Remarkably, the proposed LR+PSO model achieved the highest accuracy at 96.30%, demonstrating exceptional proficiency with an 80:20 train-test ratio. It significantly improved sensitivity to 94.44%, indicating enhanced detection of positive cases, all while maintaining the highest specificity at 97.22% and precision at 94.44% compared to other models. These results highlight a substantial improvement in integrated models, emphasizing the potential of this novel approach to enhance PCOS diagnosis in terms of accuracy and efficiency, ultimately benefiting individuals with PCOS in their treatment journey.

Hybrid machine learning techniques based on genetic algorithm for heart disease detection
Ankur Kumar, Sanjay Dhanka, Jaspreet Singh, Asim Ali Khan, and Surita Maini
World Scientific Pub Co Pte Ltd
This research study addresses the critical global health issue of heart disease (HD), emphasizing the importance of early detection for improving recovery outcomes. The authors have applied various machine learning (ML) algorithms, including logistic regression (LR), linear discriminant analysis (LDA), Gaussian naive bayes (GNB), support vector machine (SVM), and XGBoost (XGB) to classify the Statlog and Cleveland HD datasets. Performance metrics such as accuracy, precision, recall, F1-score, specificity, and Cohen’s kappa have been evaluated across these HD records. This study conducted two experiments: one using default ML classifiers and another with a hybrid genetic algorithm ML (GA-ML) model. The GA has been employed as a feature selector (FS), significantly enhancing the performance of each default classifier by selecting 9 out of 13 features. Notably, the GA-XGB model achieved the highest performance with an accuracy of 94.83%, precision of 93.33%, sensitivity of 96.55%, F1-score of 94.52%, specificity of 93.10%, Cohen’s kappa of 0.90, a positive likelihood ratio (LR[Formula: see text]) of 14, a negative likelihood ratio (LR-) of 0.037, and a diagnostic odds ratio (DOR) of 378 on the combined HD dataset. These results have been validated using a 10-fold cross-validation technique. A comparative analysis has been conducted with default ML classifiers, hybrid GA-ML classifiers, and state-of-the-art methods. The results of the GA-ML models confirm the superiority of the proposed method, offering valuable insights into advancing early detection strategies and improving heart health care outcomes.

Smart Crop Selection: Harnessing Machine Learning for Sustainable Agriculture in the Era of Industry 5.0
Ankur Kumar, Sanjay Dhanka, Rohit Bansal, Abhinav Sharma, Jaspreet Singh, Asim Ali Khan, and Surita Maini
Springer Nature Switzerland

Thermography as an Economical Alternative Modality to Mammography for Early Detection of Breast Cancer
Asim Ali Khan and Ajat Shatru Arora
Hindawi Limited
Breast cancer has become a menacing form of cancer among women accounting for 11.6% of total deaths of 9.6 million due to all types of cancer every year all over the world. Early detection increases chances of survival and reduces the cost of treatment as well. Screening modalities such as mammography or thermography are used to detect cancer early; thus, several lives can be saved with timely treatment. But, there are interpretational failures on the part of the radiologists to read the mammograms or thermograms and also there are interobservational and intraobservational differences between them. So, the degree of variations among the different radiologists in the interpretation of results is very high resulting in false positives and false negatives. The double reading can reduce the human errors involved in the interpretation of mammograms. But, the limited number of medical professionals in developing or underdeveloped countries puts a limitation on this remedial way. So, a computer-aided system (CAD) is proposed to detect the benign cases from the abnormal cases that can result in automatic detection of breast cancer or can provide a double reading in the case of nonavailability of the trained medical professionals in developing economies. The generally accepted screening modality is mammography for the early detection of cancer. But thermography has been tried for early detection of breast cancer in recent times. The high metabolic activity of the cancer cells results in an early change in the temperature profile of the region. This shows asymmetry between normal and cancerous breast which can be detected using different techniques. Thus, this work is focussed on the use of thermography in the early detection of breast cancer. An experimental study is conducted to find the results of classification accuracy to compare the efficacy of thermography and mammography in classifying the normal from abnormal ones and further abnormal ones into benign and malignant cases. Thermography is found to have classification accuracy almost at par with mammography for classifying the cancerous breasts from healthy ones with classification accuracies of thermography and mammography being 96.57% and 98.11%, respectively. Thermography is found to have much better accuracy in identifying benign cases from the malignant ones with the classification accuracy of 92.70% as compared to 82.05% with mammography. This will result in the early detection of cancer. The advantage of being portable and inexpensive makes thermography an attractive modality to be used in economically backward rural areas where mammography is not practically possible.

Fatigue Assessment of Bicep Brachii Muscle Using Surface EMG Signals Obtained from Isometric Contraction
Tripash Bansal and Asim Ali Khan
ACM
In this study, Surface EMG signals are used to analyze the progression of muscular fatigue with time by estimating the change in myoelectric properties when right bicep brachii muscle is subjected to constant force isometric contraction. Muscular fatigue most frequently occurs due to powerful utilization of a group of muscles which can lead to decline in performance or sometimes to injury and can go undetected at early stage. In this proposed method, Discrete Wavelet Transform is used to decompose the EMG signals using Daubechies type 7 wavelet with three level of decomposition. For each detailed and approximate component temporal features like Root Mean Square, and Spectral features like Mean frequency, Median frequency and Energy are evaluated. Results show that mean frequency values perform significantly better in estimating the level of muscular fatigue with time. Furthermore, using Support Vector Machine classifier, the subjects were classified into muscular and non-muscular groups and second level detailed component shows high class separability in feature space.

Classification in Thermograms for Breast Cancer Detection using Texture Features with Feature Selection Method and Ensemble Classifier
Asim Ali Khan and Ajat Shatru Arora
IEEE
the most common cancer among the women is breast cancer with very high mortality rate accounting for about 7% of the all cancer deaths (1). Though very nominal, the men too can have the chances of developing the breast cancer. The early detection can be boon for survival chances of the patients. Though Mammography is commonly accepted screening tool technique for breast cancer detection. But the thermography has the advantage of the early detection of the cancer when no masses are formed to be detected by the mammography. Moreover, mammography is a painful procedure and patient is exposedto harmful X-rays. The thermography is based on the asymmetry between affected and the normal breasts due to increased blood flow in the cancerous cells. This results in the difference in the temperature profile of the two breasts which is detected with the help of thermal imagers. The texture of bothbreasts are obtained withGabor texturefeatures. The features that can contribute to the classification are selected from the feature space of the all Gabor features extracted. Finally, the classification of the thermograms into healthy and sickcases are done using ensemble classifier. The accuracy obtained in his paper using selected Gabor features and ensemble classifier is 92.55%.

Breast Cancer Detection Through Gabor Filter Based Texture Features Using Thermograms Images
A.A. Khan and A. S. Arora
IEEE
The mortality rates in women is highest due to breast cancer among other all the cancers in developed as well as in developing countries. As evident from the facts that mortality rate of 12.7 among 1, 00, 000 in India [1] and whereas in USA, estimated deaths of 40,610 women i.e. 6.8% of all cancer deaths in 2017. Mammography is considered to be the most accepted technique for breast cancer detection. In this paper, thermography is explored as a viable alternative to the mammography. As mammography has its own drawbacks of being a painful procedure, exposure of the body to harmful Xrays. This necessitates in exploring the other modalities preferably non-contact and without using any harmful radiations. Thermography is coming out to be an alternative to the standard mammography with advantages of being noninvasive, safe, portability and cost effectiveness. The temperature pattern of the breasts changes as a result of the high increased blood flow into affected cells. This gives the way to asymmetry between normal and cancerous breast which can be detected using different techniques. In this paper, 35 normal and 35 abnormal thermograms are taken from on line DMRDatabase for Mastology Research having breast thermograms for early detection of breast cancer. The texture features of the left and right breasts are extracted using Gabor filters. The thermograms are then classified using support vector machine (SVM) based on the textural asymmetry between the breasts into normal and cancerous cases. The accuracy achieved using Gabor features and SVM classifier is 84.5% The early detection of cancer using thermography increases the survival chances of the patient considerably as it can detect the cancer in initial stages.

Computer aided diagnosis of breast cancer based on level set segmentation of masses and classification using ensemble classifiers
Asim Ali Khan and Arora AS
OMICS Publishing Group
Breast cancer has been ranked number one cancer in Indian females with rates occurrence of 25.8 per 1,00,000 females and death rate 12.7 among 1,00,000. Whereas in USA, the estimated new detected cases of breast cancer are 2,52,710 with 15% of all new cancer cases with estimated deaths of 40,610 women i.e. 6.8% of all cancer deaths in 2017. The mammograms can help an early detection of lesions by radiologists before it becomes incurable. But the degree of variations among the different radiologists is very high resulting in false positives and false negatives. So a great amount of research is focused on the design of computer aided diagnosis (CAD) for early detection of breast cancer from the mammograms. In quest of high accuracy, this paper aims at developing an automated computer aided diagnostic (CAD) system that detects the malignant neoplasms from the mammograms. A novel technique is used to remove the pectoral muscle in pre-processing stage in order to make the segmentation of suspicious masses from breasts easier. The active contour based level set method is used to segment the mammograms. The texture features being most implemented in mammographic analysis, the standard gray-level co-occurrence matrix (GLCM) texture descriptors by Harallick are extracted from the segmented images. Finally, ensemble classifier is used to classify the mammograms into normal and abnormal, and then abnormal ones into malignant and benign. The respective accuracies obtained are 97.46% and 82.05% respectively.

Computer Aided Diagnostic System for Automatic Detection of Brain Tumor Through MRI Using Clustering Based Segmentation Technique and SVM Classifier
Atanu K. Samanta and Asim Ali Khan
Springer International Publishing

Automatic detection of malignant neoplasm from mammograms
Asim Ali Khan, Mister Khan, and Ajat Shatru Arora
IEEE
Breast cancer is one of the leading cause of death in woman worldwide both in developed and developing nations as per the records from World Health Organization (WHO). The World Health organization stated that more than 1.2 million women were found with breast cancer and more than 700,000 women lost their life every year in the world [1]. Mammograms are already known for its fuzzy nature, in addition to it a fuzzy classification characteristic between malignant and benign lesions; make the detection a challenging task. Results of proper extraction of ROIs prove the successful execution of preprocessing steps like label removal, pectoral removal and de-noising. To screen out the non-mass candidates from the mass ones, segmentation, texture based feature extraction and classification using Support Vector Machine (SVM) and Artificial Neural Network (ANN) is carried out. Maximum Sensitivity of 100% in all categories proves that zero probability of missing out Normal candidate while the screening process of Malignant from set of both, overall accuracy respectively ranges from 100% to 83.33% with an average of 98.90% when Normal and Malignant are classified, overall accuracy ranges from 92.33% to 80.00% with an average 84.75% when Normal and Benign are classified, and 100% to 85.71% with an average 94.90% when Benign and Malignant are classified using SVM. Whereas classification rate with ANN classifier is able to reach approx. of 92.60%, 87.50% and 90.00% respectively.

A novel algorithm for pectoral muscle removal and auto-cropping of neoplasmic area from mammograms
M. Hanmandlu, Asim Ali Khan, and Anubhuti Saha
IEEE
Presence of pectoral muscle has always been a hindrance in neoplasm detection in screening mammography. Mediolateral-oblique (MLO) x-ray view of the breast taken while screening mammography shows the presence of pectoral muscle. The intensity range shared by pectoral muscle, masses and calcification clusters being almost the same makes pectoral muscle removal a vital or necessary step to attain proper segmentation of actual region of interest (ROI) i.e. the neoplasmic region. This paper provides a novel algorithm for automatic detection and removal of pectoral muscle along with breast boundary detection and several artefacts removal present in digital mammograms. A concatenation of an auto-cropping algorithm to pectoral removal step gives a précise RoI which helps in stepping up the lesion detection accuracy of the Computer-Aided Detection (CAD) system. This composite method has been has been implemented and applied to mini-MIAS which is one of the most challenging digital database consisting 322 MLO view mammograms. The algorithm shows an accuracy of around 83.89% on a set of 298 mammogram images.

Fuzzy PID controller: Design, tuning and comparison with conventional PID controller

asim ali khan

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Scopus Publications