1
|
Imran M, Islam Tiwana M, Mohsan MM, Alghamdi NS, Akram MU. Transformer-based framework for multi-class segmentation of skin cancer from histopathology images. Front Med (Lausanne) 2024; 11:1380405. [PMID: 38741771 PMCID: PMC11089103 DOI: 10.3389/fmed.2024.1380405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction Non-melanoma skin cancer comprising Basal cell carcinoma (BCC), Squamous cell carcinoma (SCC), and Intraepidermal carcinoma (IEC) has the highest incidence rate among skin cancers. Intelligent decision support systems may address the issue of the limited number of subject experts and help in mitigating the parity of health services between urban centers and remote areas. Method In this research, we propose a transformer-based model for the segmentation of histopathology images not only into inflammation and cancers such as BCC, SCC, and IEC but also to identify skin tissues and boundaries that are important in decision-making. Accurate segmentation of these tissue types will eventually lead to accurate detection and classification of non-melanoma skin cancer. The segmentation according to tissue types and their visual representation before classification enhances the trust of pathologists and doctors being relatable to how most pathologists approach this problem. The visualization of the confidence of the model in its prediction through uncertainty maps is also what distinguishes this study from most deep learning methods. Results The evaluation of proposed system is carried out using publicly available dataset. The application of our proposed segmentation system demonstrated good performance with an F1 score of 0.908, mean intersection over union (mIoU) of 0.653, and average accuracy of 83.1%, advocating that the system can be used as a decision support system successfully and has the potential of subsequently maturing into a fully automated system. Discussion This study is an attempt to automate the segmentation of the most occurring non-melanoma skin cancer using a transformer-based deep learning technique applied to histopathology skin images. Highly accurate segmentation and visual representation of histopathology images according to tissue types by the proposed system implies that the system can be used for skin-related routine pathology tasks including cancer and other anomaly detection, their classification, and measurement of surgical margins in the case of cancer cases.
Collapse
Affiliation(s)
- Muhammad Imran
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Mohsin Islam Tiwana
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Mashood Mohammad Mohsan
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Norah Saleh Alghamdi
- Department of Computer Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Muhammad Usman Akram
- Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| |
Collapse
|
2
|
Zeb A, Qureshi WS, Ghafoor A, Malik A, Imran M, Mirza A, Tiwana MI, Alanazi E. Towards sweetness classification of orange cultivars using short-wave NIR spectroscopy. Sci Rep 2023; 13:325. [PMID: 36609678 PMCID: PMC9822895 DOI: 10.1038/s41598-022-27297-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 12/29/2022] [Indexed: 01/08/2023] Open
Abstract
The global orange industry constantly faces new technical challenges to meet consumer demands for quality fruits. Instead of traditional subjective fruit quality assessment methods, the interest in the horticulture industry has increased in objective, quantitative, and non-destructive assessment methods. Oranges have a thick peel which makes their non-destructive quality assessment challenging. This paper evaluates the potential of short-wave NIR spectroscopy and direct sweetness classification approach for Pakistani cultivars of orange, i.e., Red-Blood, Mosambi, and Succari. The correlation between quality indices, i.e., Brix, titratable acidity (TA), Brix: TA and BrimA (Brix minus acids), sensory assessment of the fruit, and short-wave NIR spectra, is analysed. Mix cultivar oranges are classified as sweet, mixed, and acidic based on short-wave NIR spectra. Short-wave NIR spectral data were obtained using the industry standard F-750 fruit quality meter (310-1100 nm). Reference Brix and TA measurements were taken using standard destructive testing methods. Reference taste labels i.e., sweet, mix, and acidic, were acquired through sensory evaluation of samples. For indirect fruit classification, partial least squares regression models were developed for Brix, TA, Brix: TA, and BrimA estimation with a correlation coefficient of 0.57, 0.73, 0.66, and 0.55, respectively, on independent test data. The ensemble classifier achieved 81.03% accuracy for three classes (sweet, mixed, and acidic) classification on independent test data for direct fruit classification. A good correlation between NIR spectra and sensory assessment is observed as compared to quality indices. A direct classification approach is more suitable for a machine-learning-based orange sweetness classification using NIR spectroscopy than the estimation of quality indices.
Collapse
Affiliation(s)
- Ayesha Zeb
- grid.412117.00000 0001 2234 2376Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan ,grid.412117.00000 0001 2234 2376Robot Design and Development Lab. National Centre of Robotics and Automation, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan
| | - Waqar Shahid Qureshi
- Robot Design and Development Lab. National Centre of Robotics and Automation, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, 46000, Pakistan. .,School of Computer Science, Technological University Dublin, Dublin, D07 H6K8, Ireland.
| | - Abdul Ghafoor
- grid.412117.00000 0001 2234 2376Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan
| | - Amanullah Malik
- grid.413016.10000 0004 0607 1563Institute of Horticultural Sciences, University of Agriculture, Faisalabad, Pakistan
| | - Muhammad Imran
- grid.412117.00000 0001 2234 2376Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan
| | - Alina Mirza
- grid.412117.00000 0001 2234 2376Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan
| | - Mohsin Islam Tiwana
- grid.412117.00000 0001 2234 2376Robot Design and Development Lab. National Centre of Robotics and Automation, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, 46000 Pakistan
| | - Eisa Alanazi
- grid.412832.e0000 0000 9137 6644Department of Computer Science, Umm Al-Qura University, Mecca, Saudi Arabia
| |
Collapse
|
3
|
Ahmed MH, Jamshid A, Amjad U, Azhar A, Hassan MZU, Tiwana MI, Qureshi WS, Alanazi E. 3D Printable Thermoplastic Polyurethane Energy Efficient Passive Foot. 3D Print Addit Manuf 2022; 9:557-565. [PMID: 36660747 PMCID: PMC9831569 DOI: 10.1089/3dp.2021.0022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Passive energy storing prosthetics are redesigned to improve the stored and recovered energy during different phases of the gait cycle. Furthermore, the demand of the low-cost passive prosthesis that are capable of energy storing is increasing day by day especially in underdeveloping countries. This article proposes a new passive foot design that is more energy efficient if 3D printed using thermoplastic polyurethane (TPU) material. The model is built in SOLIDWORKS®, and then the finite element analysis is conducted on ANSYS®. Two models of the foot are designed with and without Steps on the toe and heel, where the difference of Steps showed difference in the energy stored in the foot during stimulation. TPU being a flexible material with high strength and durability is chosen as the material for the 3D printed foot. The analysis performed on the foot is for an 80 kg person at different angles during the gait cycle for the K2 human activity level. The results obtained indicate high energy storage ability of TPU that is 0.044 J/Kg, comparative to other materials Hytrel, Delrin, and Carbon Fiber DA that are commonly used in passive foots.
Collapse
Affiliation(s)
- Muhammad Hassaan Ahmed
- Robot Design and Development Lab (RDDL), National Centre of Robotics and Automation (NCRA), NUST College of E&ME, Rawalpindi, Pakistan
- Department of Mechanical Engineering and NUST College of E&ME, Rawalpindi, Pakistan
| | - Asharib Jamshid
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| | - Usman Amjad
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| | - Aashir Azhar
- Robot Design and Development Lab (RDDL), National Centre of Robotics and Automation (NCRA), NUST College of E&ME, Rawalpindi, Pakistan
- Department of Chemical and Material Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | | | - Mohsin Islam Tiwana
- Robot Design and Development Lab (RDDL), National Centre of Robotics and Automation (NCRA), NUST College of E&ME, Rawalpindi, Pakistan
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| | - Waqar Shahid Qureshi
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
- Department of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Eisa Alanazi
- Department of Computer Science, Umm Al-Qura University, Makkah, Saudi Arabia
| |
Collapse
|
4
|
Rehan M, Saleem MM, Tiwana MI, Shakoor RI, Cheung R. A Soft Multi-Axis High Force Range Magnetic Tactile Sensor for Force Feedback in Robotic Surgical Systems. Sensors (Basel) 2022; 22:s22093500. [PMID: 35591190 PMCID: PMC9105633 DOI: 10.3390/s22093500] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/25/2022] [Accepted: 04/29/2022] [Indexed: 12/04/2022]
Abstract
This paper presents a multi-axis low-cost soft magnetic tactile sensor with a high force range for force feedback in robotic surgical systems. The proposed sensor is designed to fully decouple the output response for normal, shear and angular forces. The proposed sensor is fabricated using rapid prototyping techniques and utilizes Neodymium magnets embedded in an elastomer over Hall sensors such that their displacement produces a voltage change that can be used to calculate the applied force. The initial spacing between the magnets and the Hall sensors is optimized to achieve a large displacement range using finite element method (FEM) simulations. The experimental characterization of the proposed sensor is performed for applied force in normal, shear and 45° angular direction. The force sensitivity of the proposed sensor in normal, shear and angular directions is 16 mV/N, 30 mV/N and 81 mV/N, respectively, with minimum mechanical crosstalk. The force range for the normal, shear and angular direction is obtained as 0–20 N, 0–3.5 N and 0–1.5 N, respectively. The proposed sensor shows a perfectly linear behavior and a low hysteresis error of 8.3%, making it suitable for tactile sensing and biomedical applications. The effect of the material properties of the elastomer on force ranges and sensitivity values of the proposed sensor is also discussed.
Collapse
Affiliation(s)
- Muhammad Rehan
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; (M.R.); (M.I.T.)
| | - Muhammad Mubasher Saleem
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; (M.R.); (M.I.T.)
- National Centre of Robotics and Automation (NCRA), Islamabad 44000, Pakistan;
- Correspondence:
| | - Mohsin Islam Tiwana
- Department of Mechatronics Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan; (M.R.); (M.I.T.)
- National Centre of Robotics and Automation (NCRA), Islamabad 44000, Pakistan;
| | - Rana Iqtidar Shakoor
- National Centre of Robotics and Automation (NCRA), Islamabad 44000, Pakistan;
- Department of Mechatronics Engineering, Air University, Islamabad 44000, Pakistan
| | - Rebecca Cheung
- Institute for Integrated Micro and Nano Systems, School of Engineering, University of Edinburgh, Scottish Microelectronics Centre, Edinburgh EH9 3FF, UK;
| |
Collapse
|
5
|
Ghumman U, Jabbar H, Tiwana MI, Khalil IU, Kunwar F. A novel approach of overtaking maneuvering using modified RG method. PLoS One 2022; 17:e0260455. [PMID: 35051201 PMCID: PMC8776333 DOI: 10.1371/journal.pone.0260455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/09/2021] [Indexed: 11/18/2022] Open
Abstract
Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical.
Collapse
Affiliation(s)
- Usman Ghumman
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| | - Hamid Jabbar
- Robot Design and Development Lab (RDDL), National Centre of Robotics and Automation (NCRA), NUST College of E&ME, Rawalpindi, Pakistan
| | - Mohsin Islam Tiwana
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
- Robot Design and Development Lab (RDDL), National Centre of Robotics and Automation (NCRA), NUST College of E&ME, Rawalpindi, Pakistan
| | - Ihsan Ullah Khalil
- Department of Electrical Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| | - Faraz Kunwar
- Department of Mechatronics Engineering, NUST College of E&ME, Rawalpindi, Pakistan
| |
Collapse
|